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    • Oseki, Yohei (5)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' 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) BASE Show details 2 Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars ... Yoshida, Ryo; Noji, Hiroshi; Oseki, Yohei. - : arXiv, 2021 BASE Show details 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 BASE Show details 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 BASE Hide details 5 Lower Perplexity is Not Always Human-Like ... Kuribayashi, Tatsuki; Oseki, Yohei; Ito, Takumi. - : arXiv, 2021 BASE Show details

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