Abstract
We investigate how to adapt simultaneous text translation methods such as wait-k and monotonic multihead attention to end-to-end simultaneous speech translation by introducing a pre-decision module. A detailed analysis is provided on the latency-quality trade-offs of combining fixed and flexible pre-decision with fixed and flexible policies. We also design a novel computation-aware latency metric, adapted from Average Lagging.- Anthology ID:
- 2020.aacl-main.58
- Volume:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Kam-Fai Wong, Kevin Knight, Hua Wu
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 582–587
- Language:
- URL:
- https://aclanthology.org/2020.aacl-main.58
- DOI:
- Cite (ACL):
- Xutai Ma, Juan Pino, and Philipp Koehn. 2020. SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 582–587, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation (Ma et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2020.aacl-main.58.pdf
- Code
- pytorch/fairseq
- Data
- MuST-C