@inproceedings{nikiforova-etal-2020-language,
title = "Language Models for Cloze Task Answer Generation in {R}ussian",
author = "Nikiforova, Anastasia and
Pletenev, Sergey and
Sinitsyna, Daria and
Sorokin, Semen and
Lopukhina, Anastasia and
Howell, Nick",
editor = "Chersoni, Emmanuele and
Devereux, Barry and
Huang, Chu-Ren",
booktitle = "Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lincr-1.4",
pages = "28--37",
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.",
language = "English",
ISBN = "979-10-95546-52-8",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Language Models for Cloze Task Answer Generation in Russian
%A Nikiforova, Anastasia
%A Pletenev, Sergey
%A Sinitsyna, Daria
%A Sorokin, Semen
%A Lopukhina, Anastasia
%A Howell, Nick
%Y Chersoni, Emmanuele
%Y Devereux, Barry
%Y Huang, Chu-Ren
%S Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-52-8
%G English
%F nikiforova-etal-2020-language
%X 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.
%U https://aclanthology.org/2020.lincr-1.4
%P 28-37
Markdown (Informal)
[Language Models for Cloze Task Answer Generation in Russian](https://aclanthology.org/2020.lincr-1.4) (Nikiforova et al., LiNCr 2020)
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.