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Oseki, Yohei (5)
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The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (1)
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 (1)
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Simple Search
Hits 1 – 5 of 5
1
Learning Argument Structures with Recurrent Neural Network Grammars
Yoshida, Ryo
;
Oseki, Yohei
In: Proceedings of the Society for Computation in Linguistics (2022)
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2
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars ...
Yoshida, Ryo
;
Noji, Hiroshi
;
Oseki, Yohei
. - : arXiv, 2021
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3
Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Noji, Hiroshi
;
Oseki, Yohei
. - : Underline Science Inc., 2021
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4
Lower Perplexity is Not Always Human-Like ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Asahara, Masayuki
;
Inui, Kentaro
;
Ito, Takumi
;
Kuribayashi, Tatsuki
;
Oseki, Yohei
;
Yoshida, Ryo
. - : Underline Science Inc., 2021
Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.405 Abstract: In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization ---\textit{the lower perplexity a language model has, the more human-like the language model is}--- in Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from ...
Keyword:
Computational Linguistics
;
Condensed Matter Physics
;
Deep Learning
;
Electromagnetism
;
FOS Physical sciences
;
Information and Knowledge Engineering
;
Neural Network
;
Semantics
URL:
https://underline.io/lecture/25824-lower-perplexity-is-not-always-human-like
https://dx.doi.org/10.48448/mjnr-8446
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5
Lower Perplexity is Not Always Human-Like ...
Kuribayashi, Tatsuki
;
Oseki, Yohei
;
Ito, Takumi
. - : arXiv, 2021
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