Abstract
As the quality of Machine Translation (MT) improves, research on improving discourse in automatic translations becomes more viable. This has resulted in an increase in the amount of work on discourse in MT. However many of the existing models and metrics have yet to integrate these insights. Part of this is due to the evaluation methodology, based as it is largely on matching to a single reference. At a time when MT is increasingly being used in a pipeline for other tasks, the semantic element of the translation process needs to be properly integrated into the task. Moreover, in order to take MT to another level, it will need to judge output not based on a single reference translation, but based on notions of fluency and of adequacy – ideally with reference to the source text.- Anthology ID:
- W17-4814
- Volume:
- Proceedings of the Third Workshop on Discourse in Machine Translation
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Bonnie Webber, Andrei Popescu-Belis, Jörg Tiedemann
- Venue:
- DiscoMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 110–121
- Language:
- URL:
- https://aclanthology.org/W17-4814
- DOI:
- 10.18653/v1/W17-4814
- Cite (ACL):
- Karin Sim Smith. 2017. On Integrating Discourse in Machine Translation. In Proceedings of the Third Workshop on Discourse in Machine Translation, pages 110–121, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- On Integrating Discourse in Machine Translation (Sim Smith, DiscoMT 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W17-4814.pdf