Papers by Anisia Katinskaia
What Do Transformers Know about Government? (2024.lrec-main)
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Jue Hou, Anisia Katinskaia, Lari Kotilainen, Sathianpong Trangcasanchai, Anh-Duc Vu, Roman Yangarber
| Challenge: | Currently, data is lacking for the research community working on grammatical constructions, and government in particular. |
| Approach: | They use transformer language models to study how government relations are encoded . they use morphologically rich languages to train a classifier capable of discovering new types of government . |
| Outcome: | The proposed classifiers can learn new types of government, the authors show . they find that the classifier can learn government relations in two languages . |
GPT-3.5 for Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Recent work shows that GPT-3.5 struggles with several error types, including punctuation mistakes, tense errors, syntactic dependencies between words, and lexical compatibility at the sentence level. |
| Approach: | They evaluate GPT-3.5 for grammatical error correction in multiple languages . they use it to re-rank correction hypotheses generated by other GEC models . |
| Outcome: | The proposed model performs well in English and Russian, but struggles with errors in other languages. |
Revita: a Language-learning Platform at the Intersection of ITS and CALL (L18-1)
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| Challenge: | Existing language-learning tools do not address the fundamental requirements of language learners and teachers. |
| Approach: | They propose a free-to-use platform for language learning beyond the beginner level . they outline the established desiderata of CALL and ITS . |
| Outcome: | The proposed platform supports language learning beyond the beginner level. |
Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning (2020.lrec-1)
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Lionel Nicolas, Verena Lyding, Claudia Borg, Corina Forascu, Karën Fort, Katerina Zdravkova, Iztok Kosem, Jaka Čibej, Špela Arhar Holdt, Alice Millour, Alexander König, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Anisia Katinskaia, Anabela Barreiro, Lavinia Aparaschivei, Yaakov HaCohen-Kerner
| Challenge: | Lack of wide-coverage and high-quality LRs is a longstanding issue in natural language processing (NLP) however, there are no large initiatives of similar scale for creating new LR or improving existing ones. |
| Approach: | They propose a generic approach to combine implicit crowdsourcing and language learning to mass-produce language resources (LRs) they describe its core paradigm that consists in pairing specific types of LRs with specific exercises . |
| Outcome: | The proposed approach can be used in several learning scenarios to produce a multitude of NLP resources and alleviate the long-standing issue of the lack of LRs. |
Linguistic Constructs Represent the Domain Model in Intelligent Language Tutoring (2023.eacl-demo)
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| Challenge: | a new language-learning platform, Revita, is being developed for language learners . the platform uses a system of linguistic constructs to represent domain knowledge . |
| Approach: | They propose to use a domain model to represent the domain knowledge of Revita's online tutoring system. |
| Outcome: | The proposed language-learning platform, Revita, is based on the domain model of linguistic constructs . the system is undergoing pilot use with hundreds of students at several universities . |
Effects of sub-word segmentation on performance of transformer language models (2023.emnlp-main)
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| Challenge: | Language models are a fundamental task in natural language processing, but few studies focus on the effect of sub-word segmentation on the performance of models. |
| Approach: | They compare GPT and BERT models trained with statistical segmentation algorithm BPE to unsupervised morphological segmentation algorithms Morfessor and StateMorph. |
| Outcome: | The proposed model trains for several languages and compares them with two unsupervised morphological segmentation algorithms. |
Semi-automatically Annotated Learner Corpus for Russian (2022.lrec-1)
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| Challenge: | Revita Learner Corpus is a semi-automatically annotated learner corpus for Russian . it is used for research in second language acquisition and foreign language teaching . |
| Approach: | They propose a semi-automatically annotated learner corpus for Russian that detects errors automatically and annotates errors by type. |
| Outcome: | The proposed corpus detects errors automatically and is annotated by type . the data is made public and the process is much cheaper and faster . |
Toward a Paradigm Shift in Collection of Learner Corpora (2020.lrec-1)
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| Challenge: | a pilot version of the Revita Learner Corpus (ReLCo) is available for Russian learners . it is collected and annotated automatically while learners practice with Revita . |
| Approach: | They present the first version of the longitudinal Revita Learner Corpus (ReLCo) for Russian . the corpus contains 8 422 sentences exhibiting several types of errors committed by learners . |
| Outcome: | The Russian version of the Revita Learner Corpus is publicly available . the pilot study shows that the corpus grows continuously while learners practice . |
Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet (2020.lrec-1)
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Christos Rodosthenous, Verena Lyding, Federico Sangati, Alexander König, Umair ul Hassan, Lionel Nicolas, Jolita Horbacauskiene, Anisia Katinskaia, Lavinia Aparaschivei
| Challenge: | Language resources (LRs) are expensive to create and maintain, and this makes it difficult to create or extend LRs. |
| Approach: | They propose to use a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet with new words. |
| Outcome: | The proposed model allows to gather 12,000 answers from learners on different question types over 16 days and shows that it is a potential tool for crowdsourcing and fostering vocabulary skills. |
Probing the Category of Verbal Aspect in Transformer Language Models (2024.findings-naacl)
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| Challenge: | a particular challenge is posed by ”alternative contexts” where either the perfective or the imperfective aspect is suitable grammatically and semantically. |
| Approach: | They investigate how pretrained language models encode the grammatical category of verbal aspect in Russian. |
| Outcome: | The proposed model has high predictive uncertainty about aspect in alternative contexts, the authors show . |