Abstract
Neural Machine Translation (NMT) systems encounter a significant challenge when translating a pro-drop (‘pronoun-dropping’) language (e.g., Chinese) to a non-pro-drop one (e.g., English), since the pro-drop phenomenon demands NMT systems to recover omitted pronouns. This unique and crucial task, however, lacks sufficient datasets for benchmarking. To bridge this gap, we introduce PROSE, a new benchmark featured in diverse pro-drop instances for document-level Chinese-English spoken language translation. Furthermore, we conduct an in-depth investigation of the pro-drop phenomenon in spoken Chinese on this dataset, reconfirming that pro-drop reduces the performance of NMT systems in Chinese-English translation. To alleviate the negative impact introduced by pro-drop, we propose Mention-Aware Semantic Augmentation, a novel approach that leverages the semantic embedding of dropped pronouns to augment training pairs. Results from the experiments on four Chinese-English translation corpora show that our proposed method outperforms existing methods regarding omitted pronoun retrieval and overall translation quality.- Anthology ID:
- 2023.emnlp-main.141
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2297–2311
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.141
- DOI:
- 10.18653/v1/2023.emnlp-main.141
- Cite (ACL):
- Ke Wang, Xiutian Zhao, Yanghui Li, and Wei Peng. 2023. PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2297–2311, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation (Wang et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.emnlp-main.141.pdf