title: Automatic Generation of Lexico-Grammatical Tests for Russian as a Foreign Language Using Predictive Language Models
metadata.name: dc.contributor.author
metadata.value: Belyi, Andrei V.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Mitrofanova, Olga A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Dubinina, Nadezhda А.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-04-19T22:24:22Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-04-19T22:24:22Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-06
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Belyi, A. V., Mitrofanova, O. A., & Dubinina N. А. (2024). Automatic Generation of Lexico-Grammatical Tests for Russian as a Foreign Language Using Predictive Language Models. The World of Russian Word, (2), 108–118. https://doi.org/10.21638/spbu30.2023.212
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu30.2023.212
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/45320
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The authors argue that in teaching a foreign language, one of the basic needs of participants of the educational process is a sufficient number of educational data. Among the tasks that contribute to the acquisition of lexical and grammatical units, gap-filling exercises with multiple-choice have become particularly popular. Nowadays, creating unique tasks manually turns out to be laborintensive. Unlike English, the development of algorithms for exercise generation for Russian is not so active, despite the existing need. In this regard, the authors propose a method for automatic generation of tasks of this type for Russian as a foreign language (RFL). The proposed method is based on distributive semantic models like word2vec and allows to create tasks based on authentic texts, it does not depend on the genre and style of the text or the corresponding language level, and can be easily adapted for other languages. To train the word2vec model, a corpus of children’s and educational literature was developed to emulate the language experience of students. In the course of the work, a web application for teachers was also launched. To assess the consistency and relevance of generated tasks, two experiments were conducted. In the first experiment, naive native speakers of the Russian language were interviewed, while in the second, a survey of experts in RFL was carried out. The high degree of correctness of the tasks and the selected distractors is proved by high scores of precision (0.8) and recall (0.91). The experts have also noted the convenience of the web application.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The research is carried out within the framework of the St. Petersburg State University research project no. 75254082
  “Modeling the communicative behavior of residents of a Russian metropolis in socio-speech and pragmatic aspects using
  artifi cial intelligence methods” and the Russian Science Foundation grant no. 21-78-10148 “Modeling the meaning of a
  word in individual linguistic consciousness based on distributive semantics”.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: The World of Russian Word;Issue 2
metadata.lang: -
metadata.name: dc.subject
metadata.value: teaching Russian as a foreign language
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: automatic generation of language exercises
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: lexical and grammatical exercises
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: gap-filling
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: multiple-choice
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Automatic Generation of Lexico-Grammatical Tests for Russian as a Foreign Language Using Predictive Language Models
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: МИРС_2-2023_108-118_Белый&Митрофанова&Дубинина.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/45320/1/%d0%9c%d0%98%d0%a0%d0%a1_2-2023_108-118_%d0%91%d0%b5%d0%bb%d1%8b%d0%b9%26%d0%9c%d0%b8%d1%82%d1%80%d0%be%d1%84%d0%b0%d0%bd%d0%be%d0%b2%d0%b0%26%d0%94%d1%83%d0%b1%d0%b8%d0%bd%d0%b8%d0%bd%d0%b0.pdf
resources.format: Adobe PDF
resources.size: 808,93 kB