Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Katinskaia, Anisia"'
Assessment of proficiency of the learner is an essential part of Intelligent Tutoring Systems (ITS). We use Item Response Theory (IRT) in computer-aided language learning for assessment of student ability in two contexts: in test sessions, and in exe
Externí odkaz:
http://arxiv.org/abs/2409.16133
Autor:
Katinskaia, Anisia, Yangarber, Roman
We investigate how pretrained language models (PLM) encode the grammatical category of verbal aspect in Russian. Encoding of aspect in transformer LMs has not been studied previously in any language. A particular challenge is posed by "alternative co
Externí odkaz:
http://arxiv.org/abs/2406.02335
Autor:
Katinskaia, Anisia, Yangarber, Roman
This paper investigates the application of GPT-3.5 for Grammatical Error Correction (GEC) in multiple languages in several settings: zero-shot GEC, fine-tuning for GEC, and using GPT-3.5 to re-rank correction hypotheses generated by other GEC models.
Externí odkaz:
http://arxiv.org/abs/2405.08469
Autor:
Hou, Jue, Katinskaia, Anisia, Kotilainen, Lari, Trangcasanchai, Sathianpong, Vu, Anh-Duc, Yangarber, Roman
This paper investigates what insights about linguistic features and what knowledge about the structure of natural language can be obtained from the encodings in transformer language models.In particular, we explore how BERT encodes the government rel
Externí odkaz:
http://arxiv.org/abs/2404.14270
Publikováno v:
This submission published in EMNLP 2023
Language modeling is a fundamental task in natural language processing, which has been thoroughly explored with various architectures and hyperparameters. However, few studies focus on the effect of sub-word segmentation on the performance of languag
Externí odkaz:
http://arxiv.org/abs/2305.05480
This paper presents the development of an AI-based language learning platform Revita. It is a freely available intelligent online tutor, developed to support learners of multiple languages, from low-intermediate to advanced levels. It has been in pil
Externí odkaz:
http://arxiv.org/abs/2212.01711
We would like to explore how morphemes can affect the performance of a language model. We trained GPT-2 and Bert model with StateMorph for both Finnish and Russian, which is a morpheme segmenting algorithm. As a comparison, we also trained a model wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9e41d74b98f99ef675ff6524019121b
http://arxiv.org/abs/2305.05480
http://arxiv.org/abs/2305.05480
We explore the importance of gamification features in a language-learning platform designed for intermediate-to-advanced learners. Our main thesis is: learning toward advanced levels requires a massive investment of time. If the learner engages in mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1593::9c34221a93ddb4f8d430db12333c0c64
http://hdl.handle.net/10138/357057
http://hdl.handle.net/10138/357057
Autor:
Nicolas, Lionel, Lyding, Verena, Borg, Claudia, Fort, Karen, Zdravkova, Katerina, Kosem, Iztok, Čibej, Jaka, Arhar Holdt, Špela, Millour, Alice, König, Alexander, Rodosthenous, Christos, Sangati, Federico, Katinskaia, Anisia, Barreiro, Anabela, Aparaschivei, Lavinia, HaCohen-Kerner, Yaakov
We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1593::3d96298724d49f9949d652ec380cc0ec
http://hdl.handle.net/10138/339393
http://hdl.handle.net/10138/339393
Autor:
Nicholas, Lionel, Lyding, Verena, Borg, Claudia, Forascu, Corina, Fort, Karen, Zdravkova, Katerina, Kosem, Iztok, Cibej, Jaka, Holdt, Spela Arhar, Millour, Alice, Konig, Alexander, Rodosthenous, Christos, Sangati, Federico, Hassan, Umair ul, Katinskaia, Anisia, Barreiro, Anabela, Aparaschivei, Lavina, HaCohen-Kerner, Yaakov, 12th edition of the Language Resources and Evaluation Conference (LREC'20)
We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3549::752584a3b9e9a9af52902efa6aa71c70
https://www.um.edu.mt/library/oar/handle/123456789/69182
https://www.um.edu.mt/library/oar/handle/123456789/69182