@inproceedings{ri-etal-2021-zero,
title = "Zero-pronoun Data Augmentation for {J}apanese-to-{E}nglish Translation",
author = "Ri, Ryokan and
Nakazawa, Toshiaki and
Tsuruoka, Yoshimasa",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.11",
doi = "10.18653/v1/2021.wat-1.11",
pages = "117--123",
abstract = "For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.",
}
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<abstract>For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.</abstract>
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%0 Conference Proceedings
%T Zero-pronoun Data Augmentation for Japanese-to-English Translation
%A Ri, Ryokan
%A Nakazawa, Toshiaki
%A Tsuruoka, Yoshimasa
%S Proceedings of the 8th Workshop on Asian Translation (WAT2021)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F ri-etal-2021-zero
%X For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.
%R 10.18653/v1/2021.wat-1.11
%U https://aclanthology.org/2021.wat-1.11
%U https://doi.org/10.18653/v1/2021.wat-1.11
%P 117-123
Markdown (Informal)
[Zero-pronoun Data Augmentation for Japanese-to-English Translation](https://aclanthology.org/2021.wat-1.11) (Ri et al., WAT 2021)
ACL