Speech Translation and the End-to-End Promise: Taking Stock of Where We Are

Matthias Sperber, Matthias Paulik


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://www.aclweb.org/anthology/2020.acl-main.661
DOI:
10.18653/v1/2020.acl-main.661
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.661.pdf
Video:
 http://slideslive.com/38928786