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        <title>Multi-Task Learning for Japanese Predicate Argument Structure Analysis</title>
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            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)</title>
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            <namePart type="given">Jill</namePart>
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    <abstract>An event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and event-noun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multi-task models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1344</identifier>
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%0 Conference Proceedings
%T Multi-Task Learning for Japanese Predicate Argument Structure Analysis
%A Omori, Hikaru
%A Komachi, Mamoru
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F omori-komachi-2019-multi
%X An event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and event-noun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multi-task models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.
%R 10.18653/v1/N19-1344
%U https://aclanthology.org/N19-1344/
%U https://doi.org/10.18653/v1/N19-1344
%P 3404-3414

Markdown (Informal)

[Multi-Task Learning for Japanese Predicate Argument Structure Analysis](https://aclanthology.org/N19-1344/) (Omori & Komachi, NAACL 2019)

• Multi-Task Learning for Japanese Predicate Argument Structure Analysis (Omori & Komachi, NAACL 2019)

ACL

• Hikaru Omori and Mamoru Komachi. 2019. Multi-Task Learning for Japanese Predicate Argument Structure Analysis. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3404–3414, Minneapolis, Minnesota. Association for Computational Linguistics.