Abstract
Document-level contextual information has shown benefits to text-based machine translation, but whether and how context helps end-to-end (E2E) speech translation (ST) is still under-studied. We fill this gap through extensive experiments using a simple concatenation-based context-aware ST model, paired with adaptive feature selection on speech encodings for computational efficiency. We investigate several decoding approaches, and introduce in-model ensemble decoding which jointly performs document- and sentence-level translation using the same model. Our results on the MuST-C benchmark with Transformer demonstrate the effectiveness of context to E2E ST. Compared to sentence-level ST, context-aware ST obtains better translation quality (+0.18-2.61 BLEU), improves pronoun and homophone translation, shows better robustness to (artificial) audio segmentation errors, and reduces latency and flicker to deliver higher quality for simultaneous translation.- Anthology ID:
- 2021.acl-long.200
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2566–2578
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.200
- DOI:
- 10.18653/v1/2021.acl-long.200
- Cite (ACL):
- Biao Zhang, Ivan Titov, Barry Haddow, and Rico Sennrich. 2021. Beyond Sentence-Level End-to-End Speech Translation: Context Helps. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2566–2578, Online. Association for Computational Linguistics.
- Cite (Informal):
- Beyond Sentence-Level End-to-End Speech Translation: Context Helps (Zhang et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.200.pdf
- Code
- bzhangGo/zero
- Data
- MuST-C