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.- Anthology ID:
- 2021.wat-1.11
- Volume:
- Proceedings of the 8th Workshop on Asian Translation (WAT2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Toshiaki Nakazawa, Hideki Nakayama, Isao Goto, Hideya Mino, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Shohei Higashiyama, Hiroshi Manabe, Win Pa Pa, Shantipriya Parida, Ondřej Bojar, Chenhui Chu, Akiko Eriguchi, Kaori Abe, Yusuke Oda, Katsuhito Sudoh, Sadao Kurohashi, Pushpak Bhattacharyya
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 117–123
- Language:
- URL:
- https://aclanthology.org/2021.wat-1.11
- DOI:
- 10.18653/v1/2021.wat-1.11
- Cite (ACL):
- Ryokan Ri, Toshiaki Nakazawa, and Yoshimasa Tsuruoka. 2021. Zero-pronoun Data Augmentation for Japanese-to-English Translation. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 117–123, Online. Association for Computational Linguistics.
- Cite (Informal):
- Zero-pronoun Data Augmentation for Japanese-to-English Translation (Ri et al., WAT 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.wat-1.11.pdf
- Data
- Business Scene Dialogue