Abstract
Over its three decade history, speech translation has experienced several shifts in its primary research themes; moving from loosely coupled cascades of speech recognition and machine translation, to exploring questions of tight coupling, and finally to end-to-end models that have recently attracted much attention. This paper provides a brief survey of these developments, along with a discussion of the main challenges of traditional approaches which stem from committing to intermediate representations from the speech recognizer, and from training cascaded models separately towards different objectives. Recent end-to-end modeling techniques promise a principled way of overcoming these issues by allowing joint training of all model components and removing the need for explicit intermediate representations. However, a closer look reveals that many end-to-end models fall short of solving these issues, due to compromises made to address data scarcity. This paper provides a unifying categorization and nomenclature that covers both traditional and recent approaches and that may help researchers by highlighting both trade-offs and open research questions.- Anthology ID:
- 2020.acl-main.661
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7409–7421
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.661
- DOI:
- 10.18653/v1/2020.acl-main.661
- Cite (ACL):
- Matthias Sperber and Matthias Paulik. 2020. Speech Translation and the End-to-End Promise: Taking Stock of Where We Are. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7409–7421, Online. Association for Computational Linguistics.
- Cite (Informal):
- Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (Sperber & Paulik, ACL 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.acl-main.661.pdf