Language Models for Cloze Task Answer Generation in Russian

Anastasia Nikiforova, Sergey Pletenev, Daria Sinitsyna, Semen Sorokin, Anastasia Lopukhina, Nick Howell

Abstract
Linguistics predictability is the degree of confidence in which language unit (word, part of speech, etc.) will be the next in the sequence. Experiments have shown that the correct prediction simplifies the perception of a language unit and its integration into the context. As a result of an incorrect prediction, language processing slows down. Currently, to get a measure of the language unit predictability, a neurolinguistic experiment known as a cloze task has to be conducted on a large number of participants. Cloze tasks are resource-consuming and are criticized by some researchers as an insufficiently valid measure of predictability. In this paper, we compare different language models that attempt to simulate human respondents’ performance on the cloze task. Using a language model to create cloze task simulations would require significantly less time and conduct studies related to linguistic predictability.
Anthology ID:
2020.lincr-1.4
Volume:
Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Emmanuele Chersoni, Barry Devereux, Chu-Ren Huang
Venue:
LiNCr
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
28–37
Language:
English
URL:
https://aclanthology.org/2020.lincr-1.4
DOI:
Bibkey:
Cite (ACL):
Anastasia Nikiforova, Sergey Pletenev, Daria Sinitsyna, Semen Sorokin, Anastasia Lopukhina, and Nick Howell. 2020. Language Models for Cloze Task Answer Generation in Russian. In Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources, pages 28–37, Marseille, France. European Language Resources Association.
Cite (Informal):
Language Models for Cloze Task Answer Generation in Russian (Nikiforova et al., LiNCr 2020)
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