Abstract
Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. Recently, Wang et al. (2018) proposed a novel reconstruction-based approach to alleviating dropped pronoun (DP) translation problems for neural machine translation models. In this work, we improve the original model from two perspectives. First, we employ a shared reconstructor to better exploit encoder and decoder representations. Second, we jointly learn to translate and predict DPs in an end-to-end manner, to avoid the errors propagated from an external DP prediction model. Experimental results show that our approach significantly improves both translation performance and DP prediction accuracy.- Anthology ID:
- D18-1333
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2997–3002
- Language:
- URL:
- https://aclanthology.org/D18-1333
- DOI:
- 10.18653/v1/D18-1333
- Cite (ACL):
- Longyue Wang, Zhaopeng Tu, Andy Way, and Qun Liu. 2018. Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2997–3002, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism (Wang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D18-1333.pdf