CN111581373B

CN111581373B - Language self-help learning method and system based on conversation

Info

Publication number: CN111581373B
Application number: CN202010394857.1A
Authority: CN
Inventors: 武林强
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-05-11
Filing date: 2020-05-11
Publication date: 2021-06-01
Anticipated expiration: 2040-05-11
Also published as: CN111581373A

Abstract

The invention discloses a language self-help learning method and system based on conversation. The language self-help learning method comprises the following steps: the learner thinks of a language knowledge point which wants to learn; searching for dialogs involving points of linguistic knowledge; the learner looks for whether there is a dialogue involving the point of knowledge of the language that the learner wants to learn: if the learning resources exist, judging whether the learning resources meet the learning requirements of the user, and then starting or ending the learning, otherwise, publishing a dialogue related to the language knowledge points which the user wants to learn, then checking and judging whether the added learning resources exist the learning resources meeting the learning requirements of the user, and then starting or ending the learning. The invention solves various problems of the traditional language learning method, really realizes the purpose of teaching according to the factors and learning one or more languages on the basis of meeting the individual learning requirements and targets.

Description

Language self-help learning method and system based on conversation

Technical Field

The invention relates to a method and a system for realizing language self-help learning based on dialogue learning through the Internet, in particular to a method and a system for contacting people with different languages and different language abilities through the Internet communication technology and helping learners to learn languages based on dialogue according to individual requirements.

Background

The importance of language need not be elaborated upon. With the rapid development of science and technology, people can understand the ideas and intentions of each other through more and more advanced machine translation technologies so as to perform good communication, but it can be found from practical communication that due to the difference of culture and thinking ways of two parties in conversation, only machine translation is required, and the two parties can generate some information errors or losses more or less during communication, even important information is lost. For example, even the most authoritative translation techniques may translate the idiom "taotai tarnish" expressing the Chinese national policy into "lying salary". Therefore, how to let the two parties who have dialogues with different languages correctly understand the meaning that the other party wants to express is a problem which needs to be solved at present, and especially under the background that the current china grows up and the culture of the traditional Chinese and the western languages is fused to a greater extent, the problem is more prominent.

The existing language learning method can not enable people in all countries to quickly and effectively learn a language, especially when western people learn Chinese with completely different languages.

The most language learning mode on the market in recent years is "i teach you," which provides standardized teaching commodities, which leads to the following problems: one is that the learner can only learn the language with teaching preparation, and for languages without teaching preparation, such as some dialects, the learner cannot learn at all unless the learner personally speaks the dialect to learn with a local person. Secondly, because the teaching materials prepared by the teaching preparation of people, such as teaching materials, are standard, and the language levels of learners are different, the learners are difficult to quickly and directly find the learning materials which are just suitable for the learners. Thirdly, the learner's requirements for language learning are different, and the learner only needs to communicate with foreigners if the learner learns the foreign language, that is, the learner only needs to learn to listen and speak and does not need to learn to read and write. And some learners only need to be able to understand the dialect spoken by the family, that is, the learners only need to learn the ability to understand the language and do not need to speak the ability to read and write the language. However, the teaching materials of the teaching commodities are synchronously learned by listening, speaking, reading and writing, which undoubtedly improves the difficulty of language learning, prolongs the time of language learning, and more importantly, defeats the enthusiasm of language learning.

With the development of science and technology, methods for helping people learn languages appear at present. For example, chinese patent No. 201610941341.8 entitled "a language learning method and apparatus" discloses a tool for enabling people to read the translation information of the corresponding video subtitles while playing the video. The invention provides a tool for learning language for people, but has limited improvement on the learning efficiency of learners, strengthens the corresponding relation between foreign language and learner's native language, prolongs the reaction time of learners using foreign language in real life due to the influence of the native language, and can not cultivate the thinking mode of the learners' foreign language. For another example, patent No. 201710444581.1 entitled "an online interactive language learning system based on network" discloses a system for online interactive language learning by learners in different countries through internet technology, which achieves the purpose of learning by learners in different countries, but still has the following problems: 1) the method has the advantages that each learning can be carried out only by matching the foreign language companions which have the same language level with the learners, want to discuss the same topic, want to learn the language of the other party and are on line at the same time, conditions are very harsh, and the matching to the proper choice is not easy, and particularly, when the population difference of two learners is large, the condition that a plurality of users on one population cannot be matched with the companions is inevitably generated; 2) a user speaking a vulnerable language cannot be matched with a speech companion; 3) even if pairing is successful, intervention by teachers with bilingual ability is still required, and the number of teachers is limited, so that the teachers cannot be matched properly with learners who learn on the internet.

In addition, there are some new instant messenger software on the market, which adds an instant machine translation function and a companion matching function and attempts to allow learners to learn foreign languages by chatting with foreigners, and the main problems of the instant messenger software are, in addition to the above-mentioned difficulty in matching, more importantly, the difficulty in advancing the language level even after a long time of communication due to the fact that the communication between both parties is targetless and unplanned.

In summary, the above-mentioned existing language learning methods do not really solve the problem of how to enter personalized or improve the communication effectiveness of a language.

Disclosure of Invention

The invention aims to provide a dialogue-based language self-help learning method and a dialogue-based language self-help learning system, which solve various problems of the traditional language learning method, really realize the purpose of learning one or more languages on the basis of meeting the individual learning requirements and targets of the language.

In order to achieve the purpose, the invention adopts the following technical scheme:

a language self-help learning method based on conversation is characterized by comprising the following steps:

1) the learner thinks of a language knowledge point which wants to learn;

2) the learner searches the dialogue relating to the language knowledge point which the learner wants to learn through the given search condition;

3) displaying all the conversations meeting the search conditions;

4) the learner looks for whether there is a dialogue involving the point of knowledge of the language that the learner wants to learn: if yes, entering 5), otherwise, jumping to 6);

5) the learner judges whether the dialogue related to the language knowledge point which the learner wants to learn and the learning resource correspondingly displayed aiming at the dialogue are the learning resource meeting the learning requirement of the learner or not: if yes, 10) is entered, otherwise, 11) is skipped;

6) the learner inputs the dialogue relating to the language knowledge point which the learner wants to learn and then makes a publication;

7) the learner views the learning resources to which the dialog relating to the point of linguistic knowledge he wants to learn is added: if the added learning resources are only machine resources, entering 8), otherwise, if the added learning resources have at least one learning resource added by the learning resource adder besides the machine resources, entering 9);

8) the learner judges whether the machine resource is a learning resource meeting the learning requirement of the learner: if yes, 10) is entered, otherwise, 11) is skipped;

9) the learner searches whether the learning resources meeting the learning requirement of the learner exist in all the displayed learning resources: if yes, 10) is entered, otherwise, 11) is skipped;

10) the learner starts to learn the learning resource;

11) the study is finished.

A dialogue-based language self-learning system, comprising:

a search unit for the learner to search for a dialogue relating to a point of knowledge of a language that the learner wants to learn by a given search condition;

a display unit for displaying the learning resources;

the publishing unit is used for publishing after the learner inputs the dialogue related to the language knowledge point which the learner wants to learn;

a machine resource adding unit for adding a machine resource;

and the user adding learning resource unit is used for adding learning resources by the learning resource adder.

A computer device comprising a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein: the processor implements the dialog-based language self-help learning method by executing a computer program.

A computer storage medium storing a computer program executable by a computer, characterized in that: and executing a computer program to realize the language self-help learning method based on the conversation.

The invention has the advantages that:

on one hand, the invention contacts people with different languages and different language abilities through the internet communication technology, so that learners can freely search and obtain learning resources meeting the learning requirements of the learners, realizes personalized learning and progress, and finally achieves the purpose of learning the language according to the learning requirements and the target of the learners.

Drawings

Fig. 1 is a flow chart of the implementation of the dialogue-based language self-help learning method of the invention.

Detailed Description

As shown in fig. 1, the language self-help learning method based on conversation of the present invention includes the following steps:

1) the learner thinks of a language knowledge point which wants to learn;

2) the learner searches the dialogue relating to the language knowledge point which the learner wants to learn through the given search condition (such as the search keyword, etc.), wherein: the search range is the conversation itself and the translation in the learning resource corresponding to the conversation, and the translation comprises a machine translation and a translation added by a learning resource adder;

3) displaying all the conversations meeting the search conditions;

4) the learner looks for whether there is a dialogue involving the point of knowledge of the language that the learner wants to learn: if the current signal exists, entering 5), otherwise, if the current signal does not exist, jumping to 6);

5) the learner judges whether the dialogue related to the language knowledge point which the learner wants to learn and the learning resource correspondingly displayed aiming at the dialogue are the learning resource meeting the learning requirement of the learner or not: if yes, entering 10), otherwise, if not or the learner can not judge, jumping to 11);

6) the learner inputs and publishes the dialog (such as the dialog conceived by the learner or the dialog actually encountered by the learner) related to the language knowledge point which the learner wants to learn;

10) the learner starts to learn the learning resource;

11) the study is finished.

In step 3), all dialogs meeting the search condition and at least one translation corresponding to each dialog are displayed, so that the learner can more accurately find whether the dialog related to the language knowledge point which the learner wants to learn by looking up the dialogs and the translations thereof, wherein: the displayed dialogue or the translation corresponding to the dialogue accords with the search condition; if the language of the dialog is the same as the native language of the learner, the language of the translation correspondingly displayed by the dialog is the language which the learner wants to learn, and if the language of the dialog is different from the native language of the learner, the language of the translation correspondingly displayed by the dialog is the native language of the learner.

In step 5), when the learner views the dialog:

if the language of the dialog is the same as the native language of the learner, the language of the machine translation provided for the dialog is the language that the learner wants to learn, and if the language of the dialog is different from the native language of the learner, the language of the machine translation provided for the dialog is the native language of the learner, wherein:

in addition to the dialog itself, the learning resources displayed correspondingly to one dialog at least include machine resources, the machine resources are machine translation translations and machine dubbing added to the dialog and the machine translation translations, that is, the language self-help learning system must provide a machine translation and machine dubbing learning resource for one dialog, of course, the machine translation may be correct or wrong, the pronunciation quality of the machine dubbing is not limited, and only the machine translation is audibly played.

In practical implementation, before a learner starts searching in the language self-help learning system, the learner needs to select own native language and a language desired to learn.

In order to create a multilingual learning resource platform for learners, the method of the present invention does not impose any limitation on the language of the learning resource, i.e., the learning resource can include multiple languages, not limited to the learner's native language and the language the learner wants to learn, so that the language (or spoken language) involved in the dialog is not necessarily the same as the learner's native language at that time. For example, if a learner whose native language is spanish publishes a conversation using spanish, which relates to a point of knowledge of the language that the learner currently wants to learn, then when the learner currently views the conversation published by a learner whose native language is spanish, the language of the machine-translated translation of the conversation is displayed using spanish while the conversation is displayed using spanish. The purpose of this design is: 1) the probability that the learner finds the resources which accord with the learner is increased. 2) The interest of learning is increased.

In step 6), the learner, while inputting the dialogue content relating to the knowledge point of the language that the learner wants to learn, may also input auxiliary information associated with the dialogue to help the learning resource adder understand the dialogue content, wherein: the auxiliary information comprises any one or any combination of explanatory words, pictures, video or audio.

In step 5), step 8) and step 9), the learner can help himself judge whether the learning resource is a learning resource meeting the self learning demand by looking at the auxiliary judgment information relating to the learning resource adder and the auxiliary judgment information relating to the learning resource.

In the present invention, the auxiliary judgment information related to the learning resource adder includes, without limitation, the user name and language competence level (such as the native language mastered by the learning resource adder, the foreign language name, and the mastery degree of these languages) of the learning resource adder, and other competence information (such as a known teacher, etc.) that can help the learner to judge the relevant situation of the learning resource adder.

In the invention, the auxiliary judgment information related to the learning resource comprises information which can help a learner to judge the relevant condition of the learning resource, such as content category marking information of the learning resource, credibility marking information of the learning resource and the like, and is not limited.

In the present invention, the content category marking information of the learning resource is used to indicate the specific category of the learning resource, and may be marked by various names, without limitation. For example, if the content category flag information of "native-language mandarin transliteration" is displayed below a translation given by a learning resource adder, it indicates that the translation is a translation translated according to the transliteration principle at the native-language level of mandarin chinese. For another example, if content category label information of "slow non-native english" is displayed under a dubbing given by a learning resource adder, it indicates that the dubbing is an english non-native level and the speech rate is a slow dubbing.

In the invention, the credibility mark information of the learning resources comprises a hundred percent credibility mark, a high-probability credibility mark, a medium-high-probability credibility mark and a medium-low-probability credibility mark, and the marks can be expressed by various names without limitation. For example, one hundred percent of trustworthy tags may be expressed as official certified translations. As another example, the high probability trustworthiness token may be expressed as a mountain certified translation, indicating that the learning resource is a user certified translation named "mountain" that is officially certified as having some public awareness. For another example, the high-probability trusted mark may be expressed as a trusted user authentication translation, which indicates that the learning resource is a user-authenticated translation that is authenticated by the authority as having no bad operation record and passed the language ability authentication. As another example, the medium to low probability trustworthiness token may be expressed as a tentative certificate.

In addition, in practical implementation, the official authentication trusted user can be evaluated through measures such as technical screening of AI and the like, secondary manual screening of organization professionals and the like, and the official language capability authentication can be evaluated according to the mother language, the accent, other grasped foreign language capability certification documents (such as examination scores, recorded videos or audios) and the like provided by the user.

In actual implementation, the learning resources are added by the learner himself or by other learning resource addicts other than the learner, wherein: when the learner adds the learning resources for the self-published conversation, the learner is used as a learning resource adder to add the learning resources;

the step of adding the learning resources for the dialogue by the learning resource adder comprises the following steps:

a) a learning resource adder (learner self or other learning resource adder) search session;

b) the learning resource adder judges whether the learning resource adder can directly add learning resources to the conversation; if yes, entering d), otherwise, if not, jumping to c);

c) the learning resource adder judges whether the learning resource adder can add the learning resource for the dialogue with the help of the learning resource given by the machine resource adder or other learning resource adders; if yes, entering d), otherwise, if not, jumping to a) or ending;

d) the learning resource adder adds a learning resource for this dialog, wherein: automatically adding machine dubbing to the translation added by the learning resource adder;

e) and giving out and displaying auxiliary judgment information related to the learning resource adder and auxiliary judgment information related to the learning resource for the added learning resource.

The learner collects the learning resources so as to check the original learning resources at any time or check newly added learning resources in the original learning resources. Therefore, in the future, on one hand, learners can check whether learning resources meeting learning requirements are newly added in the learning resources collected by the learners, and on the other hand, when the learners forget the contents of the learned learning resources, the learners can quickly search the learning resources collected by the learners, so that review and consolidation can be carried out until the learning resources are solidified in the brains of the learners.

In the present invention:

learners refer to people who need to learn a certain language or multiple languages according to their learning requirements and goals.

For the learner, the language to be learned refers to the language that the learner wants to learn, and if the learner's native language is chinese and the learner wants to learn english, the language to be learned is english. The language to be learned may be the learner's own native language, the dialect related to the native language, or other languages besides the native language, and the language to be learned may be the language that the learner knows but is not expert, etc., without limitation.

The language knowledge points are a word, a word (vocabulary), a translation of a sentence or a speech, dubbing, a cultural background and grammatical knowledge related to the speech, and the like.

For learners, the knowledge points related to the language which the learners want to learn by themselves are expressed by conversation or translation of the conversation, and the translation can be machine translation or translation added by a learning resource adder.

The conversation refers to the content of mutual communication between characters in language, and the conversation at least comprises one sentence of one person, namely the conversation can be one sentence or a plurality of sentences.

Learning resources refer to the dialog itself and the points of linguistic knowledge associated with the dialog.

The language of the dialogue has no limitation, and the learner can input the dialogue according to the language ability of the learner. A dialog may be composed of multiple languages. For example, a learner has a native Chinese level, a foreign English beginner level. He can enter the following dialog:

role of student: teacher, I have a question asking you. Native language, Chinese

Role of student: long time no se, foreign language, english

Role of student: is there a syntax error for this sentence? Native language, Chinese

The role of the teacher: this sentence was originally a chinese english sentence. But is easy to understand because of too many people who say it. Has been accepted as a permanent term. It is not grammatically so common.

Native language, Chinese

Role of student: good, thank you, teacher. I understand it. Native language, Chinese

The language of the learning resources other than the dialogue is not limited, and the learning resource adder can input the learning resources according to the language capability of the learning resource adder. He can input his own native language, and also can input a foreign language with a high or low degree of his own grasp. Since the auxiliary judgment information relating to the learning resource adder and the auxiliary judgment information relating to the learning resource are present below the learning resource inputted, even if the foreign language inputted by the user is a wrong foreign language, it does not matter, because the user can know the information through the auxiliary judgment information.

The above-described "language of conversation" and "language of learning resource other than conversation" may be combined into what is called "language of learning resource" because the conversation itself is also a learning resource. Therefore, the following steps are described: the language of the learning resource is not limited, and learners or learning resource adding persons can provide the learning resource at will according to the language ability of the learners or learning resource adding persons.

The language desired to be learned can be any language the learner sees in the learning resources, without limitation.

In addition, the search content input by the learner based on the given search condition can use any language without limitation, and the language self-help learning system only searches for the dialogue and the translation matched with the input search content. Here, the search principle can be reasonably designed according to actual requirements, as long as the search principle can help the learner to quickly find the dialog the learner wants to find. For example, when the language self-help learning system searches based on the input search content, the search content may be searched after being converted according to the language of the search object, for example, in searching the input search content "do help? "will not only contain" need help? "conversation, translation found, will also contain" Need any help? ", and search for content" need help? "the same meaning of the dialogue and translation of English content is found.

The invention also provides a language self-help learning system based on the conversation, which comprises:

a search unit for the learner to search the dialogue relating to the language knowledge point which the learner wants to learn by a given search condition (such as a search keyword, etc.);

a display unit for displaying the learning resources;

a publishing unit, which is used for the learner to publish after inputting the dialog (such as the dialog supposed by the learner or the dialog actually encountered by the learner) related to the language knowledge point which the learner wants to learn;

a machine resource adding unit for adding a machine resource, specifically, for performing machine translation on the conversation by the machine translation software and adding machine dubbing to the conversation and the machine translation by the machine dubbing software;

and the user adding learning resource unit is used for adding learning resources by the learning resource adder, wherein machine dubbing is automatically added to the translation added by the learning resource adder.

For the publishing unit, the learner, while inputting the contents of the dialog relating to the point of knowledge of the language that the learner wants to learn, may also input auxiliary information associated with the dialog to help the learning resource adder understand the contents of the dialog, wherein: the auxiliary information comprises any one or any combination of explanatory words, pictures, video or audio.

The dialogue-based language self-help learning system further comprises an auxiliary judgment information generation unit used for giving auxiliary judgment information related to learning resource addition persons and auxiliary judgment information related to learning resources aiming at the added learning resources.

In the present invention, the display unit is further configured to display the auxiliary judgment information relating to the learning resource adder and the auxiliary judgment information relating to the learning resource.

The language self-help learning system based on the conversation also comprises a collection unit used for collecting the learning resources by the learner so as to check the original learning resources or check the newly added learning resources in the original learning resources at any time.

In the invention, the language self-help learning system is a software system installed in the electronic equipment, the electronic equipment can carry out wireless communication with a server of a remote control center through an internet communication technology, and the language self-help learning system is installed in the server of the remote control center.

The invention also proposes a computer device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein: the processor implements the dialogue-based language self-help learning method of the invention by executing the computer program.

The present invention also proposes a computer storage medium storing a computer program executable by a computer, wherein: the language self-help learning method based on the dialogue can be realized by executing the computer program.

The following illustrates the implementation of the process of the invention:

example 1:

a learner who is English zero-base, Chinese is spoken in the mother language, and is named Zhang III, the language learning purpose is as follows: when a waiter at a restaurant, the waiter can communicate with guests in English, and the method of the invention is used for learning English according to the following steps:

first, Zhang three parties experience their waiters based on, i.e., their first round of interaction with guests generally is to ask the guest: "do it need help? ". Therefore, Zhang three want to know how this word is spoken in English in the same scene.

And secondly, Zhang III inputs a keyword of 'do it needs help' in the language self-help learning system for searching.

In example 1, the language self-learning system searches its database as follows: the search scope is the translation in the dialogue and the learning resource corresponding to the dialogue, and the translation comprises a machine translation and a translation added by the learning resource adder. Therefore, the language self-help learning system finds all conversations in the search scope that contain the same or similar to "do it needs help". It should be noted that the search principle in example 1 is only an example, and the search principle in actual implementation may be reasonably designed according to actual requirements as long as the search principle can help the learner to quickly find the dialog that the learner wants to find.

Based on the search principle, it is assumed that the language self-help learning system finally finds the following dialogs:

dialog 1:

a conversation taking place in a restaurant

The server: you good! Is help required?

Customer: you good! I need a four-person table!

The server: good, please follow me!

And 2, conversation:

A conversation in a restaurant.

Waiter：Welcome！Need any help？

John：No，thank you.I found the wrong place.

in example 1, the reason why the language self-learning system found dialog 1 is that there is a character in dialog 1 to speak: "the server: you good! Is help required? ", and the keyword" do it needs help "is included in the introduction of this role. The reason why the language self-help learning system finds the conversation 2 is that one role in the conversation 2 speaks: "Waiter: welcome! Need any help? "and a translation corresponding to the angular utterance is" attendant: welcome! Is help required? ", which contains the keyword" do it needs help ".

And thirdly, displaying all the conversations which are found in the last step and meet the search conditions in a list form by the language self-help learning system for three-page viewing. The dialog list is displayed as follows:

dialog 1:

a conversation taking place in a restaurant

A conversation in a restaurant

The server: you good! Is help required?

Waiter：Hello！Do you need any help？

Customer: you good! I need a four-person table!

Customer：Hello！I need a table for four！

The server: good, please follow me!

Waiter：OK，please follow me！

And 2, conversation:

A conversation in a restaurant.

talking in a restaurant.

Waiter：Welcome！Need any help？

The server: welcome! Do help?

John：No，thank you.I found the wrong place.

John: not, thanks. I find a mistake.

As described above, all dialogs matching the search condition and a translation corresponding to each dialog are displayed, so that the learner can more accurately find whether there is a dialog relating to the point of knowledge of the language that the learner wants to learn by looking at the dialogs and their translations. Of course, each dialog meeting the search condition is not limited to displaying one translation below, and a plurality of translations may be displayed.

In example 1, the translation is displayed on the principle of:

if the language of the dialog is the same as the native language of the learner, the language of the translation correspondingly displayed by the dialog is the language which the learner wants to learn, and if the language of the dialog is different from the native language of the learner, the language of the translation correspondingly displayed by the dialog is the native language of the learner. In practical implementation, the display principle of the translation language is not limited as long as it can help the learner understand the dialogue.

And fourthly, checking whether the conversations 1 and 2 listed above are conversations related to the language knowledge points which the user wants to learn.

In example 1, zhang san is required as a conversation that occurs when the waiter has just taken the guest at the restaurant, so zhang san needs to read the conversation or translation of the conversation listed in the conversation list. For dialog 1, since dialog 1 is written in chinese and the mother language of zhang san is also chinese, zhang san can easily determine that dialog 1 is a dialog relating to a point of linguistic knowledge that the user wants to learn by directly reading dialog 1. For dialog 2, since dialog 2 is written in english, and english base of zhang san is zero, zhang san can easily judge that dialog 2 is a dialog relating to a point of knowledge of the language that the user wants to learn by reading the chinese translation of dialog 2. That is, zhang san can easily determine that both dialogs 1 and 2 are dialogs related to the point of language knowledge that the user wants to learn, and then proceed to the next step. However, assuming that zhang san does not find a dialog relating to a point of knowledge of the language that she wants to learn, it jumps to the sixth step.

And fifthly, assuming that three times select the dialog 1 as the dialog related to the language knowledge point which the user wants to learn, three times look at the dialog 1 and the corresponding learning resource. The auxiliary judgment information relating to the learning resource adder and the auxiliary judgment information relating to the learning resource in the dialog 1 are displayed as follows, for example:

the dialog 1 provider: lisi, mother language and Chinese

A conversation native language, Chinese, occurring in the restaurant

A conversation in a restaurant

The server: you good! Is help required? Native language, Chinese

Waiter：Hello！Do you need any help？

Customer: you good! I need a four-person table! Native language, Chinese

Customer：Hello！I need a table for four！

The server: good, please follow me! Native language, Chinese

Waiter：OK，please follow me！

Here, "lie four, native language, chinese" is auxiliary judgment information concerning the learning resource adder of the dialog 1 provider. Zhang III can be judged according to the auxiliary judgment information, and the conversation 1 is provided by a user with the native language capability of Chinese and the user name of Li IV. The "native language and chinese" displayed after the utterance of each character in the dialog 1 is auxiliary judgment information concerning the learning resources about the utterance of the character. For example, Zhang III may be based on "Administrator: you good! Is help required? "auxiliary judgment information relating to learning resources" native language-chinese "judgment" behind this angular utterance: you good! Is help required? "this part of the character utterance is an utterance of a character of the native language level in chinese.

Zhang III wanted to learn about what the waiter was in the context of the restaurant attending guests, and what we say "do you need help? "expressing the pronunciation of english language with the same meaning, therefore, dialog 1 itself is not a learning resource required by zhang san. Therefore, zhang san can further view the role talk "server: you good! Is help required? "whether the learning resources corresponding to the displayed learning resources have the learning resources meeting the learning requirements of the user.

Since the role speaks "attendant: you good! Is help required? "the language type of itself is Chinese, and is the same as the mother language of zhang san, so the language of the machine resource provided by the language self-help learning system for the role speech is the language english that zhang san wants to learn. For example, the character in dialog 1 speaks "attendant: you good! Is help required? "the corresponding learning resources are shown below:

and (3) machine translation:

hello! Do you need a new and help? English unauthenticated

Jack, mother language, english:

hello! Can I help you? Native language, English, transliteration and official authentication

The machine translation and machine dubbing presented above are machine resources that the language self-help learning system must provide for a conversation.

In example 1, the english level of zhang is zero, and with the help of "auxiliary judgment information concerning learning resource adder" and "auxiliary judgment information concerning learning resource", he can easily judge that the mother language of a user named Jack is english, he provides a sum of "waiter: you good! Is help required? "corresponding translation translated according to the transliteration principle and having english language, and this translation is officially certified and is a translation which can be learned with ease. Although Zhang III does not understand the translation, Zhang III can therefore determine that the pronunciation corresponding to the translation may contain a learning resource meeting the learning requirement of Zhang III. Zhang III then further looks at all dubbing learning resources that the translation corresponds to showing. Because the language self-help learning system provides machine dubbing for all dialogues and translations without requirements on the quality of the dubbing of Zhang III, for example, Zhang III does not require learning American pronunciation or English pronunciation, the machine dubbing corresponding to the translation can meet the learning requirements of Zhang III.

Assuming three choices of dialog 2 as the dialog relating to the point of linguistic knowledge that one wants to learn, three views of dialog 2 and its corresponding learning resources. The auxiliary judgment information relating to the learning resource adder and the auxiliary judgment information relating to the learning resource in the dialog 2 are displayed as follows, for example:

the dialog 2 provider: essar, mother language, English

A conversation in a restaurant. Native language English

Talking in a restaurant.

Waiter: welcome! Need any help? Native language English

The server: welcome! Do help?

John: no, thank you.I found the wreng place, native language, English

John: not, thanks. I find a mistake.

Wherein "dialog 2 provider: "Essar, mother language, and english" displayed later "is auxiliary judgment information concerning the learning resource adder of the dialog 2 provider. And Zhang III can judge that the conversation 2 is provided by a user with the native language capability of English and the user name of Essar according to the auxiliary judgment information. The "native language and english" displayed after the speech of each character in the dialog 2 is auxiliary judgment information relating to learning resources for the character speech. For example, zhang san can determine that the speech of each character preceding "native language/chinese" is a speech of a character having a native english level.

Zhang III wanted to learn about what the waiter was in the context of the restaurant attending guests, and what we say "do you need help? "expressing the pronunciation of the english language with the same meaning, and therefore, the translation" waiter: welcome! Do help? "the machine dubbing after the corresponding character speaking is the learning resource needed by zhang san. Therefore, Zhang III can further check whether the learning resources displayed corresponding to the speaking of the role have the learning resources meeting the learning requirements of the user.

Since the character speaks "Waiter: welcome! Need any help? The language type of the Chinese language self-help learning system is English, which is different from the parent language of Zhang III, so that the language of the machine resource provided by the language self-help learning system for the role speech is the parent language of Zhang III Chinese. For example, the character speaks "Waiter: welcome! Need any help? "the corresponding learning resources are shown below:

and (3) machine translation:

you good! What help you need? Chinese non-authentication

Jim, mother language, english:

welcome! Need help? Native language, English, official certification

Tom, mother tongue, english:

welcome! Can I help you? Native language, English, official certification

In example 1, the english level of zhang is zero, and with the help of "auxiliary judgment information concerning learning resource adder" and "auxiliary judgment information concerning learning resource", he can easily judge that the native languages of the users with user names Jim and Tom are both english, and they provide different descriptions of english native levels expressing the same meaning in the same context authenticated by the authorities. The corresponding machine dubbing in these words is a learning resource meeting the requirement of Zhang III learning, in this case, Zhang III can freely select the machine dubbing behind the translation provided by the Jim or Tom user, so that Zhang III starts learning, repeatedly listens and imitates the pronunciation of the dubbing.

Assuming that the requirement of Zhang III for learning language is not as above, and that his own English needs to be a standard American English pronunciation, then after looking at dialog 1 and dialog 2 themselves and their corresponding displayed learning resources, he finds that there is no learning resource that is authenticated as an American pronunciation, in which case Zhang III can end the learning.

And sixthly, opening three dialogues according to the language capability and the actual language using scene, and assuming a conversation and inputting the conversation into the language self-help learning system for publishing. For example, Zhang three entered the following dialog using his native language:

this is a conversation that occurs when a restaurant attendant meets a guest and enters the restaurant

The server: you good! Welcome you to visit the restaurant.

The server: is asking for help? Are you a few meals?

Customer: 4 bits.

The server: sorry, there is now no large table! You are in a row of a number bar.

Zhang III submits and delivers after inputting the above dialogue.

In this step, zhang san has not only entered the conversation itself, but also interpreted words that help the learning resources adder understand the conversation, as in the first sentence "this is the conversation that occurs when a restaurant attendant meets the guest and enters the restaurant" in example 1.

Seventhly, Zhang III checks the added learning resources after publishing the dialog input by the user.

Suppose that the added learning resources seen by zhang san zhang are only machine resources, for example, the machine resources are shown as follows:

and (3) machine translation:

hello! Do you need a new and help? English-mountain authentication

Therefore, three pairs of machine resources are judged whether to be learning resources meeting the learning requirements of the machine. For example, zhang san can judge with the help of "auxiliary judgment information relating to learning resource adder" and "auxiliary judgment information relating to learning resource", that although the learning resource is a machine resource, the learning resource is a translation authenticated by a user named "mountain" who is officially authenticated to have a certain public awareness. Zhang III considers that the purpose of learning English by oneself is not to do work requiring one hundred percent of translation accuracy such as translating business contracts, but only to communicate with people. Therefore, the machine dubbing after the machine translation is considered to be a learning resource meeting the learning requirement of the user, and in view of the situation, Zhang III can select the machine dubbing after the machine translation to start learning, and repeatedly listen to and imitate the pronunciation of the dubbing.

Of course, zhang san may also determine that the user's authenticated machine resource is insufficient as a credit, and he must learn one hundred percent correct learning resource after official authentication, based on which zhang san can end learning.

Assume that the added learning resources seen by Zhang three reviews have organic resources and the learning resources added by the learning resource adder are shown, for example, as follows:

and (3) machine translation:

hello! Do you need a new and help? English official certification

King five, mother language, chinese:

help? Non-native language, English, meaning translation, unauthenticated

Therefore, zhang san can search for whether a learning resource meeting the learning requirement exists in all the displayed learning resources, for example, zhang san can judge that 1 official authenticated one hundred percent correct translation and 1 unauthenticated translation exist currently with the help of the "auxiliary judgment information related to learning resource adder" and the "auxiliary judgment information related to learning resources". However, since Zhang III is zero basic in English, it is considered that the official translation, although being definitely correct, is too long spelled and is not suitable for the language level. The translation which is not authenticated cannot be guaranteed to be correct, but is very short and suitable for the language level of the user. Zhang III considers that the translated version provided by Wang Wu is often seen in the self-help language learning system, and under comprehensive consideration, Zhang III decides to select the dubbing corresponding to the translated version provided by Wang Wu as a learning resource meeting the learning requirement of the user. Here, Zhang III may select a machine dubbing, or a dubbing provided by Wang Wu or a dubbing provided by another user (if provided by another user).

Of course, zhang san may also determine that all the displayed learning resources do not meet the learning requirement of the user, and zhang san may end the learning based on this condition.

In practical application, on one hand, Zhang III judges that the learning resources meet the learning requirements of the user, Zhang III can collect the learning resources so as to be convenient for finding out the learning resources collected by the user quickly in the future when the user forgets the content of the learned learning resources, so that review consolidation can be carried out until the learning resources are solidified into the brain of the user, and on the other hand, Zhang III judges that the learning resources do not meet the learning requirements of the user in the fifth step and the seventh step, Zhang III can also collect the learning resources so as to check newly increased learning resources in the original learning resources at any time and judge whether the newly increased learning resources are the learning resources meeting the learning requirements of the user.

Example 2:

an English zero-base, Chinese spoken by mother language, Zhang three user (either learner or learning resource adder other than learner) can add learning resource for dialogue as follows:

step one, Zhang III searches the dialogue which the user wants to add learning resources in the language self-help learning system. For example, the search dialogue is started after the keyword "week" is input by zhang san, the language self-help learning system searches its database according to the search principle in the second step in the above example 1, and then displays all the found dialogs meeting the search condition and a translation corresponding to each dialogue in a list form as follows for zhang san to view.

Dialog 1:

father: today is the day of the week, is your knowledge?

Dad：What day is it today，do you know？

And (3) son: know, saturday.

Son：Yes，Saturday.

And 2, conversation:

Dad：What day is it today？

father: what is today?

Son：I don′t know.

And (3) son: is not known.

And secondly, judging whether the user can directly add learning resources for the conversation by Zhang III:

suppose three views dialog 1, because dialog 1 is written in chinese and the native language of three views is also in chinese, three views can easily determine that they can add learning resources to dialog 1 directly. Here, the process of adding learning resources to the dialog 1 and giving the auxiliary judgment information related to the learning resource adder and the auxiliary judgment information related to the learning resource by the language self-help learning system will not be described in detail, and please refer to the process of adding learning resources to the dialog 2 and giving the auxiliary judgment information related to the learning resource adder and the auxiliary judgment information related to the learning resource by the language self-help learning system in the fourth and fifth steps.

Suppose that zhang san looks at dialog 2 because dialog 2 is written in english, and zhang san does not understand any english, zhang san does not understand what this dialog says, and in view of this situation, continues with the next step.

Thirdly, Zhang III continuously judges whether learning resources can be added to the dialogue 2 or not based on the learning resources such as the machine translation correspondingly displayed by the dialogue 2.

For example, the character in dialog 2 speaks "Dad: what day is it today? "the learning resources correspondingly displayed are as follows:

and (3) machine translation:

father: what is today? Chinese non-authentication

King five, mother language, chinese-english bilingual:

father: today is the day of the week? Native language, Chinese, official authentication

With the help of the translation provided by the learning resource adder with the user name of king five, Zhang three can understand that the role speaks "Dad: what day is it today? "what is meant by expression. At this time, zhang san may choose to add a learning resource for dialog 2, for example zhang san may be "Dad: what day is it today? "add translation, based on which case, proceed to the next step. Of course, if Zhang III cannot add a learning resource for dialog 2 based on other learning resources other than dialog 2, then Zhang III may end the addition process or return to the first step to additionally search for other dialogs.

And fourthly, Zhang three adds learning resources for the dialogue 2.

For example, Zhang three is "Dad: what day is it today? "Add translation" fath: today's day? ". Zhang III shows that the translated text level of the Zhang III is 'mother language-Chinese' when the translated text is submitted. And thirdly, after the translation is submitted, the self-help language learning system automatically adds machine dubbing to the translation, and then continues to the fifth step.

Fifthly, aiming at learning resources added by Zhang III, the language self-help learning system is used for translating the language into a language translation of' dad: today's day? "auxiliary judgment information concerning the learning resource adder and auxiliary judgment information concerning the learning resource are given and displayed.

For example, since the user name of zhang san is zhang san and the user is authenticated as a user whose native language is chinese, the auxiliary judgment information relating to the learning resource adder is displayed as "zhang san · native language · chinese". Further, since the translated sentence is input three times, it is described that the translated sentence level of the self-translation is "native language-chinese", and the translated sentence is just submitted and is not authenticated, the auxiliary judgment information relating to the learning resource is displayed as "native language-chinese-unauthenticated".

In summary, after zhang san adds a translation, the character in dialog 2 speaks "Dad: what day is it today? "the learning resources shown below are as follows:

and (3) machine translation:

father: what is today? Chinese non-authentication

King five, mother language, chinese-english bilingual:

Zhang three, mother language, Chinese:

father: today's day? Native language, Chinese, unauthenticated

Here, in the learning resource displayed above, "dad: today's day? Subsequent to the mother language, Chinese, unauthenticated

The language self-help learning system automatically becomes a translation' father: today's day? "added machine dubbing.

Then, zhang san completes the addition of the dialog 2 learning resource.

The invention has the advantages that:

The method provides a language personalized learning mode, and is not limited to learning several languages and which languages. When the invention is applied to learning foreign languages, people do not need to waste time to recite knowledge (such as a large amount of words) which can not be used in life, people do not need to learn a foreign language in a mode of listening, speaking, reading and writing simultaneously, people can achieve the purpose of learning by learning a dialogue in a certain context, and the learning mode is a personalized language learning mode which can be used by people according to the learning requirements of people, thereby really achieving the purpose of teaching by factors.

The invention has the characteristics of convenient implementation and wide interaction range, and learners only need to search the learning resources suitable for the learners to learn, thereby being convenient and fast.

The above description is of the preferred embodiment of the present invention and the technical principles applied thereto, and it will be apparent to those skilled in the art that any changes and modifications based on the equivalent changes and simple substitutions of the technical solutions of the present invention are within the protection scope of the present invention without departing from the spirit and scope of the present invention.

Claims

1. A language self-help learning method based on conversation is characterized by comprising the following steps:

1) the learner thinks of a language knowledge point which wants to learn;

3) displaying all the conversations meeting the search conditions;

10) the learner starts to learn the learning resource;

11) finishing the learning;

wherein:

for learners, the language knowledge points related to the learners, which contain the language knowledge points or do not contain the language knowledge points but are associated with the language knowledge points, are expressed by conversation or translated version of the conversation;

learning resources are added by the learner himself or by other learning resource addicts other than the learner, wherein: when the learner adds the learning resources for the self-published conversation, the learner is used as a learning resource adder to add the learning resources; the step of adding the learning resources for the dialogue by the learning resource adder comprises the following steps:

a) a learning resource adder search dialogue;

b) the learning resource adder judges whether the learning resource adder can directly add learning resources to the conversation; if yes, entering d), otherwise, jumping to c);

c) the learning resource adder judges whether the learning resource adder can add the learning resource for the conversation with the help of the machine resource; if yes, entering d), otherwise, jumping to a) or ending;

d) the learning resource adder adds learning resources for the conversation;

the learning resources refer to the conversation itself and language knowledge points associated with the conversation, multiple languages can appear in the learning resources, the language related to the conversation is the same as or different from the native language of a learner, and the language knowledge points are characters, words, sentences or translations or dubbing of a section of conversation or cultural background or grammatical knowledge related to the back of a section of conversation;

the dialogue refers to the content of mutual communication between characters in language, the dialogue is a plurality of sentences, the learning resources displayed correspondingly for one dialogue at least comprise machine resources, and the machine resources comprise machine translation.

2. A dialogue-based language self-learning method as recited in claim 1, wherein:

in the step 3), all dialogs meeting the search condition and at least one translation corresponding to each dialog are displayed, so that the learner can find whether a dialog relating to the language knowledge point which the learner wants to learn by looking up the dialogs and the translations thereof, wherein: the displayed dialogue or the translation corresponding to the dialogue accords with the search condition; if the language of the dialog is the same as the native language of the learner, the language of the translation correspondingly displayed by the dialog is the language which the learner wants to learn, and if the language of the dialog is different from the native language of the learner, the language of the translation correspondingly displayed by the dialog is the native language of the learner.

3. A dialogue-based language self-learning method as recited in claim 1, wherein:

in the step 5), when the learner views the conversation: if the language of the dialog is the same as the native language of the learner, the language of the machine translation provided for the dialog is the language that the learner wants to learn, and if the language of the dialog is different from the native language of the learner, the language of the machine translation provided for the dialog is the native language of the learner, wherein: the machine resources are machine translation and machine dubbing added to the conversation and machine translation.

4. A dialogue-based language self-learning method as recited in claim 1, wherein:

in the step 6), the learner inputs the dialogue content related to the language knowledge point which the learner wants to learn, and simultaneously inputs auxiliary information which is associated with the dialogue and helps the learning resource adder to understand the dialogue content, wherein: the auxiliary information includes any one or a combination of any of explanatory text, pictures, video or audio.

5. A dialogue-based language self-learning method as recited in claim 1, wherein:

in the step 5), the step 8) and the step 9), the learner assists the self-judgment of whether the learning resource is the learning resource meeting the self-learning requirement by looking at the auxiliary judgment information relating to the learning resource adder and the auxiliary judgment information relating to the learning resource.

6. A dialogue-based language self-learning method as recited in claim 5, wherein:

the auxiliary judgment information related to the learning resource adder comprises a user name and a language ability level of the learning resource adder;

the auxiliary judgment information related to the learning resource comprises content category marking information of the learning resource and credibility marking information of the learning resource.

7. A dialogue-based language self-learning method as recited in claim 5, wherein:

said step d) of the learning resource adder adding said learning resource for the dialogue further comprises automatically adding a machine dubbing to the translation added by the learning resource adder;

the learning resource adder adding said learning resources for the dialog further comprises the steps of: e) and giving the auxiliary judgment information related to the learning resource adder and the auxiliary judgment information related to the learning resource for the added learning resource and displaying the auxiliary judgment information.

8. A dialogue-based language self-learning method as recited in claim 1, wherein:

the learner collects the learning resources so as to check the original learning resources at any time or check newly added learning resources in the original learning resources.

9. A dialogue-based language self-learning system, comprising:

a display unit for displaying the learning resources;

a machine resource adding unit for adding a machine resource;

the user adds the unit of learning resources, is used for learning the resource adder to add the learning resources;

wherein:

learning resources are added by the learner himself or by other learning resource addicts other than the learner, wherein: when the learner adds the learning resources for the self-published conversation, the learner is used as a learning resource adder to add the learning resources; in the unit of adding learning resources by users, the learning resource adder adding learning resources for the dialogue comprises the following steps:

a) a learning resource adder search dialogue;

d) the learning resource adder adds learning resources for the conversation;

10. A dialog-based language self-learning system as claimed in claim 9, wherein:

with the posting unit, the learner, while inputting the contents of the dialog relating to the point of knowledge of the language that the learner wants to learn, also inputs auxiliary information associated with the dialog to assist the learning resource adder in understanding the contents of the dialog, wherein: the auxiliary information includes any one or a combination of any of explanatory text, pictures, video or audio.

11. A dialog-based language self-learning system as claimed in claim 9, wherein:

the dialogue-based language self-help learning system further comprises an auxiliary judgment information generation unit for giving auxiliary judgment information related to learning resource adders and auxiliary judgment information related to learning resources for the added learning resources;

the display unit is also used for displaying auxiliary judgment information related to the learning resource adder and auxiliary judgment information related to the learning resource.

12. A dialog-based language self-learning system as claimed in claim 9, wherein:

the language self-help learning system based on the conversation also comprises a collecting unit used for collecting the learning resources by the learner so as to check the original learning resources at any time or check newly added learning resources in the original learning resources.

13. A computer device comprising a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein: a processor implements the dialog-based language self-learning method of any one of claims 1 to 8 by executing a computer program.

14. A computer storage medium storing a computer program executable by a computer, characterized in that: executing a computer program to implement the dialog-based language self-learning method of any of claims 1 to 8.