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CN113822330A - Noise reduction device and method based on natural language inference classification data set - Google Patents

Noise reduction device and method based on natural language inference classification data set Download PDF

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CN113822330A
CN113822330A CN202110918801.6A CN202110918801A CN113822330A CN 113822330 A CN113822330 A CN 113822330A CN 202110918801 A CN202110918801 A CN 202110918801A CN 113822330 A CN113822330 A CN 113822330A
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徐波
赵象三
宋晖
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Donghua University
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Abstract

本发明公开了一种基于自然语言推断分类数据集的降噪装置及方法。本发明的数据格式转换模块通过对关系分类数据集中的各类特征构造模板，将关系分类数据集中的各个三元组转换成自然语言推断中的假设，将对应的文本语料转换成自然语言推断中的前提；若原始数据集中能划分出高质量的标注数据，直接使用该数据集作为训练集使用监督学习训练自然语言推断模型，若当前数据集噪声比例较大且人工标注代价高昂，通过当前模型在验证集上的降噪效果作为“反馈”以强化学习的方法训练自然语言推断模型的参数；所述数据集降噪模板通过训练好的自然语言推断模型对远程监督得到的关系分类数据集进行评估，按照得分选取置信度高的数据集作为降噪后的数据集。

Figure 202110918801

The invention discloses a noise reduction device and method based on natural language inference classification data set. The data format conversion module of the present invention converts each triple in the relationship classification data set into a hypothesis in the natural language inference by constructing templates for various features in the relationship classification data set, and converts the corresponding text corpus into the natural language inference. If the original data set can be divided into high-quality labeled data, directly use the data set as the training set to train the natural language inference model using supervised learning. The noise reduction effect on the validation set is used as "feedback" to train the parameters of the natural language inference model with reinforcement learning; the data set noise reduction template is used for the relationship classification data set obtained by remote supervision through the trained natural language inference model. For evaluation, the dataset with high confidence is selected as the denoised dataset according to the score.

Figure 202110918801

Description

Noise reduction device and method based on natural language inference classification data set
Technical Field
The invention belongs to the technical field of data processing methods, and particularly relates to a remote supervision relation classification data set noise reduction device and method based on natural language inference.
Background
The task of relationship classification is to predict the semantic relationship between two entities from a given text. Knowledge of the relationships of entities is essential to many downstream applications, such as knowledge graph completion and question answering tasks. The relational classification task typically relies on large-scale manual annotation data, which is expensive and time-consuming. To address this problem, remote supervision is often used to automatically annotate large volumes of corpora. Remote supervision is based on the assumption that: if a sentence contains an entity pair in the knowledge base, the entity pair in the sentence can be considered to have a relationship with the same entity pair in the knowledge base. Although this method can automatically obtain large-scale labeled data, it introduces noise problem.
Currently, there are two approaches to solving the noise problem of remotely supervised relational classification datasets. The first approach is to use multi-instance learning to tolerate the noise of the data set: the training data is divided into a number of packets, each packet containing a number of sentences that refer to the same pair of entities, and the model is then trained and tested in a packet-level fashion. However, such methods do not perform well in sentence-level prediction. The second method is to find noisy data directly: reinforcement learning or counterlearning is typically used to select high quality data or eliminate noisy data. However, their computational overhead is high and the performance of these methods is to be improved.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a remote supervision relation classification data set noise reduction device and method based on natural language inference. The method converts an original data set of relational classification into a natural language inference data set, uses a supervised learning training model when a large amount of supervised data which is correctly labeled and distributed in accordance with a full-scale remote supervised relational classification data set can be provided, or else uses reinforcement learning to train the model under the condition of not depending on labeled data required by the supervised learning, finally uses the natural language inference model to evaluate the relational classification data set, and selects high-quality data as an optimized data set according to evaluation scores.
The technical scheme of the invention is specifically introduced as follows:
the invention provides a noise reduction device for deducing a classification data set based on natural language, which comprises:
a data format conversion module for converting the classified data set into a natural language inferred data set;
the natural language inference model training module is used for training the converted natural language inference data set, the natural language inference model training module uses a supervised learning training model when being capable of providing a large amount of supervised data which are correctly labeled and are distributed in accordance with the full-scale remote supervision relation classification data set, and if the supervised data cannot be provided, the natural language inference model training module uses a reinforcement learning method to train the model;
and the data set noise reduction module is used for optimizing the remote supervision relation classification data set by utilizing the trained natural language inference model.
A noise reduction method for deducing a noise reduction device of a classification data set based on natural language comprises the following steps:
converting the format of the data set, converting the relation classification data set into a natural language inference data set;
training a natural language inference model, namely training the model by using a supervised learning training model when high-quality supervised data can be provided, and training the model by using a reinforcement learning method when the high-quality supervised data cannot be provided;
and denoising the data set, and optimizing the remote supervision relation classification data set through a trained natural language inference model.
The noise reduction device mainly comprises a data format conversion module, a natural language inference model training module and a data set noise reduction module; wherein: the data format conversion module converts each triple in the relational classification dataset into an assumption in natural language inference by constructing a template for each type of feature in the relational classification dataset and converts the corresponding text corpus into a premise in natural language inference; the natural language inference model training template is divided into two conditions, wherein one condition is that high-quality labeled data can be divided from an original data set, the data set can be directly used as a training set to train the natural language inference model by supervised learning, and the other condition is that the current data set has a large noise proportion and high manual labeling cost, and the noise reduction effect of the current model on a verification set can be used as feedback to train parameters of the natural language inference model by a reinforcement learning method; and the data set noise reduction template evaluates the relation classification data set obtained by remote supervision through a trained natural language inference model, and selects a data set with high confidence coefficient as a noise-reduced data set according to the score.
Compared with the prior art, the invention has the beneficial effects that:
1. the method provided by the invention belongs to the field of directly eliminating the noise in the remote supervision relation classification data set, and does not depend on the data format of a packet level, so that the data set can be optimized by using the data set noise reduction device provided by the invention in relation classification data sets of any forms.
2. Other methods of noise reduction using reinforcement learning simply use sentence coding and category coding for the coding of data to splice, but the present invention converts the noise discovery problem into a natural language inference problem: it is assumed that this cannot be deduced from the premises. There are currently many effective models for natural language inference problems. Therefore, the method has higher calculation efficiency and better effect.
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FIG. 1 is a detailed flow chart of the noise reduction method for a remote supervised relational classification dataset based on natural language inference of the present invention.
Fig. 2 is a template sample diagram of the data format conversion module of the present invention.
FIG. 3 is a schematic diagram of an example of a data noise reduction module of the present invention.
Detailed Description
The technical scheme of the invention is explained in detail in the following by combining the drawings and the examples.
A noise reduction device for a remote supervision relation classification data set based on natural language inference comprises the following modules:
(1) a data format conversion module: and respectively constructing corresponding templates according to the semantics of various relations in the relation classification data set, converting the triples in the relation classification into assumptions in natural language inference, and taking texts in the relation classification data set as the preconditions in the natural language inference to realize the construction of a natural language inference training set.
(2) A natural language inference model training module: when the original data can provide a large amount of supervised data which is correctly labeled and is distributed in accordance with a full-scale remote supervision relation classification data set, the supervised learning training model can be used, and when the original data contains a large amount of noise, and the cost of obtaining a large-scale clean data set for the supervised learning is high, the reinforcement learning method is used for training the natural language inference model under the condition of not depending on labeled data required by the supervised learning.
(3) A dataset denoising module: and scoring the relation classification data set obtained by remote supervision through a trained natural language inference model, and selecting data with high confidence coefficient as an optimized data set according to the score.
A noise reduction method for classifying a data set noise reduction device based on a remote supervision relationship specifically comprises the following steps:
the method comprises the following steps: and converting the format of the data set. And constructing corresponding templates according to the semantics of various relations in the relation classification data set, then taking the texts in the relation classification data set as the premise in natural language inference, and converting the triples corresponding to the texts into the hypothesis in the natural language inference through the templates to realize the construction of the natural language inference training set.
Step two: and training a natural language inference model. When the original data can provide a large amount of supervised data which is correctly labeled and is distributed in accordance with the full-scale remote supervision relation classification data set, the supervised learning training model can be used, and when the original data contains a large amount of noise and the cost of obtaining a large-scale clean data set for supervised learning is high, the reinforcement learning method is used for training the natural language inference model.
Step three: and a data set noise reduction step. And (4) scoring the remote supervision relation classification data set by using the natural language inference model trained in the step two, and then selecting data with high confidence coefficient as the optimized data set according to the score.
The invention converts the noise discovery problem of the remote supervision relational classification data set into the natural language inference problem, scores the relational classification data set by using the trained natural language inference model, and filters the data set based on the scores to obtain a clean data set.
A large-scale relational classification data set can be obtained through a remote supervision mode. Although the way in which the data set is constructed using remote supervision is quite efficient, the data set obtained in this way usually contains a lot of noise. The invention aims to find noise data in a data set and eliminate the noise data so as to obtain a high-quality relational classification data set.
As will be explained in detail below.
1. Data format conversion module
The format of the relation classification data set obtained by remote supervision is (h, t, r, text), wherein the text is a text, the h and the t are two entities in the text, and the r is the relation embodied in the text by the entity pair. The relational classified data is now converted into a natural language inferred data input format (P, H), where P is a premise and H is a hypothesis. The specific conversion method is as follows: firstly, respectively constructing corresponding templates according to the semantics of various relations in the relation classification data set, as shown in fig. 1, then converting the triples (H, t, r) in the original sample into the hypothesis H through the corresponding templates, and converting the text in the original sample into the hypothesis P. In this way, the natural language inference input format corresponding to the samples in all the original data sets can be obtained.
2. Natural language inference model training module
When a sufficiently high quality supervised data set is available, the natural language inference model can be trained by supervised learning directly using the natural language inference data set obtained by the data format conversion module.
When a large amount of supervised data which are correctly labeled and are distributed in accordance with a full-scale remote supervision relation classification data set cannot be obtained, the natural language inference model is trained by using reinforcement learning under the condition of not depending on labeled data required by supervision learning, and the specific training method comprises the following steps: as shown in fig. 2, a batch of original relational classification data sets are selected, format conversion is performed on the original relational classification data sets, then the original relational classification data sets are scored through a natural language inference model, and the corresponding relational classification data sets are selected according to the scores. And training a relation classifier by using the selected relation classification data set, taking the expression of the relation classifier on the verification set as the feedback of reinforcement learning, and updating the natural language inference model according to the selection result and the corresponding feedback. The specific parameter updating formula is as follows:
Figure RE-GDA0003371512430000051
wherein
Figure RE-GDA0003371512430000052
Refers to the parameters of the natural language inference model, beta refers to the learning rate, xvAnd rvInput and annotation, respectively, to a verification set, DBRefers to a batch of data in the training set, BsIt is referred to the size of the batch of data,
Figure RE-GDA0003371512430000053
refer to the current natural language inference model at DBThe selection of (1) is 0 or 1, where 1 indicates the data is left, 0 indicates the data is discarded, and fθIs using DBAdopt
Figure RE-GDA0003371512430000054
A relational classification model trained from the screened data set, F1 (F)θ(xv),rv) Is a relational classification model fθThe F1 values on the validation set, δ is the moving average of the F1 values,
Figure RE-GDA0003371512430000055
is a natural language inference model in parameters
Figure RE-GDA0003371512430000056
Lower pair DBAdopt
Figure RE-GDA0003371512430000057
Probability of screening.
3. Data set noise reduction module
The data set denoising module is used for scoring and screening a full amount of remote supervision relation classification data sets. The relational classification data set can be converted into a corresponding natural language inference model input format through the data format conversion module, then a fully trained natural language inference model can be obtained through the natural language inference model training module, and the data set is scored by using the natural language inference model. The lower the score is, the more the assumption cannot be derived from the premise, that is, the less the original data triple is found in the corresponding text, so that the data with the higher score is selected as the optimized data set, and the data with the lower score is removed from the original data set as noise. A specific example of this module is shown in fig. 3.

Claims (8)

1.一种基于自然语言推断分类数据集的降噪装置，其特征在于，包括：1. a noise reduction device based on natural language inference classification data set, is characterized in that, comprises: 将分类数据集转换成自然语言推断数据集的数据格式转换模块；A data format conversion module that converts classification datasets into natural language inference datasets; 对转换后的自然语言推断数据集进行训练的自然语言推断模型训练模块，所述自然语言推断模型训练模块在能提供大量标注正确且分布同全量远程监督关系分类数据集一致的监督数据的情况下使用监督学习训练模型，若不能提供，则使用强化学习的方法对模型进行训练；A natural language inference model training module for training the converted natural language inference data set, where the natural language inference model training module can provide a large number of supervised data that are correctly labeled and whose distribution is consistent with the full remote supervision relationship classification data set. Use supervised learning to train the model, if not available, use reinforcement learning to train the model; 利用训练好的自然语言推断模型对远程监督关系分类数据集进行优化的数据集降噪模块。A dataset noise reduction module optimized for remotely supervised relation classification datasets using a trained natural language inference model. 2.根据权利要求1所述的基于自然语言推断分类数据集的降噪装置，其特征在于，上述数据格式转换模块依据关系分类数据集中的各类关系的语义分别构造对应模板，将关系分类中的三元组转换成自然语言推断中的假设，将关系分类数据集中的文本作为自然语言推断中的前提，实现自然语言推断训练集的构造。2. The noise reduction device based on the natural language inference classification data set according to claim 1, wherein the data format conversion module respectively constructs corresponding templates according to the semantics of various relationships in the relationship classification data set, and converts the relationship classification data into corresponding templates. The triples of , are converted into hypotheses in natural language inference, and the text in the relational classification dataset is used as the premise in natural language inference to realize the construction of natural language inference training set. 3.根据权利要求1所述的基于自然语言推断分类数据集的降噪装置，其特征在于，上述自然语言推断模型训练模块，当原始数据能提供标注正确且分布同全量远程监督关系分类数据集一致的标注数据的时候使用监督学习训练模型，当原始数据包含大量噪声，使用强化学习的方法在不依赖监督学习所需的标注数据的情况下对自然语言推断模型进行训练。3. the noise reduction device based on natural language inference classification data set according to claim 1, it is characterized in that, above-mentioned natural language inference model training module, when the original data can provide labeling is correct and distributed with the full amount of remote supervision relationship classification data set Use supervised learning to train the model when the data is consistently labeled. When the original data contains a lot of noise, the reinforcement learning method is used to train the natural language inference model without relying on the labeled data required for supervised learning. 4.根据权利要求1所述的基于自然语言推断分类数据集的降噪装置，其特征在于，上述数据集降噪模块通过训练好的自然语言推断模型对远程监督得到的关系分类数据集进行打分，按照分数选取置信度高的数据作为优化后的数据集。4. the noise reduction device based on natural language inference classification data set according to claim 1, is characterized in that, above-mentioned data set noise reduction module scores the relation classification data set obtained by remote supervision through trained natural language inference model , and select the data with high confidence as the optimized data set according to the score. 5.基于权利要求1所述的基于自然语言推断分类数据集降噪装置的降噪方法，其特征在于，包括如下步骤：5. based on the denoising method of the denoising device based on natural language inference classification data set according to claim 1, is characterized in that, comprises the steps: 数据集格式转换，将关系分类数据集转换成自然语言推断数据集；Dataset format conversion, converting relational classification datasets into natural language inference datasets; 自然语言推断模型训练，在能提供大量标注正确且分布同全量远程监督关系分类数据集一致的监督数据的时候使用监督学习训练模型，无法提供的时候使用强化学习的方法对模型进行训练；For training of natural language inference models, supervised learning is used to train the model when a large amount of supervised data with correct annotations and the distribution is consistent with the full remote supervised relation classification data set can be provided, and the reinforcement learning method is used to train the model when it cannot be provided; 数据集降噪，通过训练好的自然语言推断模型对远程监督关系分类数据集进行优化。The dataset is denoised, and the remote supervised relation classification dataset is optimized by a trained natural language inference model. 6.根据权利要求5所述的降噪方法，其特征在于，上述数据格式转换过程中，依据关系分类数据集中的各类关系的语义分别构造对应模板，将关系分类中的三元组转换成自然语言推断中的假设，将关系分类数据集中的文本作为自然语言推断中的前提，实现自然语言推断训练集的构造。6. The noise reduction method according to claim 5, wherein in the above-mentioned data format conversion process, corresponding templates are respectively constructed according to the semantics of various relationships in the relationship classification data set, and the triples in the relationship classification are converted into Assumptions in natural language inference, take the text in the relational classification data set as the premise in natural language inference, and realize the construction of natural language inference training set. 7.根据权利要求5所述的降噪方法，其特征在于，上述自然语言推断模型训练过程中，当原始数据能提供较大量标注正确且分布同全量远程监督关系分类数据集一致的监督数据的时候可以时候用监督学习训练模型，当原始数据包含大量噪声，而获得大规模干净的用于监督学习的数据集代价很大的时候使用强化学习的方法对自然语言推断模型进行训练。7. The noise reduction method according to claim 5, wherein, in the training process of the above-mentioned natural language inference model, when the original data can provide a large amount of supervised data that is correctly labeled and distributed with the full amount of remote supervision relationship classification data sets. It is time to use supervised learning to train the model. When the original data contains a lot of noise, and it is very expensive to obtain a large-scale and clean data set for supervised learning, use the reinforcement learning method to train the natural language inference model. 8.根据权利要求5所述的降噪方法，其特征在于，上述数据集降噪过程中，通过训练好的自然语言推断模型对远程监督得到的关系分类数据集进行打分，按照分数选取置信度高的数据作为优化后的数据集。8. The noise reduction method according to claim 5, wherein, in the noise reduction process of the data set, the relation classification data set obtained by remote supervision is scored by the trained natural language inference model, and the confidence level is selected according to the score. The high data is used as the optimized data set.
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