Sergey Pletenev


LIORI at the FinCausal 2021 Shared task: Transformer ensembles are not enough to win
Adis Davletov | Sergey Pletenev | Denis Gordeev
Proceedings of the 3rd Financial Narrative Processing Workshop


Language Models for Cloze Task Answer Generation in Russian
Anastasia Nikiforova | Sergey Pletenev | Daria Sinitsyna | Semen Sorokin | Anastasia Lopukhina | Nick Howell
Proceedings of the Second Workshop on Linguistic and Neurocognitive Resources

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.