Department of General Linguistics

Cikkünkben bemutatjuk a laikusoknak címzett hivatalos szövegek osztályozási kísérletét felügyelt gépi tanuló algoritmusok segítségével. Vizsgálatunkhoz szakértők által, kézzel készített korpuszt használunk, amely közérthetőre fogalmazott és még átfogalmazás előtt álló mondatokat tartalmazott. Célunk ezzel egy olyan gépi tanult modell készítése, amely alkalmas lehet a szakértők figyelmét felhívni azon mondatokra egy-egy hivatalos szövegben, amelyek további megfontolást érdemelnek a szöveg közérthetőbbre alakítása során, ezzel gyorsítva a szakértői munkát. A kísérletet pilot jelleggel végezzük, az eredmények függvényében korszerűbb módszerek (pl. LSTM, BERT) esetleges kipróbálása előtt, a tapasztalatokat pedig a fentiek szerinti bináris klasszifikációs problémára jellemzően értékeljük.

The current article briefl y presents a pilot machinelearning experiment on the classifi cation of offi cial texts addressed to lay readers with the use of support vector machine as a baseline and fastText models. For this purpose, a hand-crafted corpus was used, created by the experts of the National Tax and Customs Administration of Hungary under the offi ce's Public Accessibility Programme. The corpus contained sentences that were paraphrased or completely rewritten by the experts to make them more readable for lay people, as well their original counter pairs. The aim was to automatically distinguish between these two classes by using supervised machine-learning algorithms. If successful, such a machine-learning-based model could be used to draw the attention of experts involved in making the texts of offi cial bodies more comprehensible to the average reader to the potentially problematic points of a text. Therefore, the process of rephrasing such texts could be sped up drastically. Such a rephrasing (considering, above all, the needs of the average reader) can improve the overall comprehensibility of offi cial (mostly legal) texts, and therefore supports access to justice, the transparency of governmental organizations and, most importantly, improves the rule of law in a given country.

The current article briefly presents a pilot machine-learning experiment on the classification of official texts addressed to lay readers with the use of support vector machine as a baseline and fastText models. For this purpose, a hand-crafted corpus was used, created by the experts of the National Tax and Customs Administration of Hungary under the office’s Public Accessibility Programme. The corpus contained sentences that were paraphrased or completely rewritten by the experts to make them more readable for lay people, as well their original counter pairs. The aim was to automatically distinguish between these two classes by using supervised machine-learning algorithms. If successful, such a machine-learning-based model could be used to draw the attention of experts involved in making the texts of official bodies more comprehensible to the average reader to the potentially problematic points of a text. Therefore, the process of rephrasing such texts could be sped up drastically....

Harnessing the power of Deep Learning is becoming commonplace nowadays, and the legal field is no exception. Most solutions embrace supervised approaches that require a lot of labeled data. Active Learning is a technique that exploits the already labeled data hence optimizing the time spent for labeling, saving time and costs. In this paper, the efficiency of Active Learning is investigated on a real-world hierarchical, multi-class multi-labeled dataset consisting of Hungarian legal documents from the taxation domain. Three different vectorization techniques (TF-IDF, fastText, BERT) are compared. The results suggest that by applying Active Learning, 40-60% less data is sufficient compared to random sampling and that lowering the batch size in an active learning cycle has a modest effect on reaching optimal performance.

The paper investigates how sentential predicates influence whether an indefinite (weak) subject has weak or strong interpretation. It is argued that these interpretational possibilities are determined by the specifying or non-specifying character of the predicate, which in turn depends on other predicate properties. The results of examining telic/atelic, bounded/unbounded, stage-level/individual-level distinctions is that it is telicity and locatedness that can make a predicate specifying. The main claim is that the whole story is the result of a general specifying criterion. English, Hungarian and French data are considered during the argumentation.