@inproceedings{takahashi-etal-2019-machine,
title = "Machine Comprehension Improves Domain-Specific {J}apanese Predicate-Argument Structure Analysis",
author = "Takahashi, Norio and
Shibata, Tomohide and
Kawahara, Daisuke and
Kurohashi, Sadao",
editor = "Fisch, Adam and
Talmor, Alon and
Jia, Robin and
Seo, Minjoon and
Choi, Eunsol and
Chen, Danqi",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5814",
doi = "10.18653/v1/D19-5814",
pages = "98--104",
abstract = "To improve the accuracy of predicate-argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable. We focus on a specific domain, specifically Japanese blogs on driving, and construct two wide-coverage datasets as a form of QA using crowdsourcing: a PAS-QA dataset and a reading comprehension QA (RC-QA) dataset. We train a machine comprehension (MC) model based on these datasets to perform PAS analysis. Our experiments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.",
}
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<abstract>To improve the accuracy of predicate-argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable. We focus on a specific domain, specifically Japanese blogs on driving, and construct two wide-coverage datasets as a form of QA using crowdsourcing: a PAS-QA dataset and a reading comprehension QA (RC-QA) dataset. We train a machine comprehension (MC) model based on these datasets to perform PAS analysis. Our experiments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.</abstract>
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%0 Conference Proceedings
%T Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis
%A Takahashi, Norio
%A Shibata, Tomohide
%A Kawahara, Daisuke
%A Kurohashi, Sadao
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F takahashi-etal-2019-machine
%X To improve the accuracy of predicate-argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable. We focus on a specific domain, specifically Japanese blogs on driving, and construct two wide-coverage datasets as a form of QA using crowdsourcing: a PAS-QA dataset and a reading comprehension QA (RC-QA) dataset. We train a machine comprehension (MC) model based on these datasets to perform PAS analysis. Our experiments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.
%R 10.18653/v1/D19-5814
%U https://aclanthology.org/D19-5814
%U https://doi.org/10.18653/v1/D19-5814
%P 98-104
Markdown (Informal)
[Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis](https://aclanthology.org/D19-5814) (Takahashi et al., 2019)
ACL