Abstract
Recent works in neural machine translation have begun to explore document translation. However, translating online multi-speaker conversations is still an open problem. In this work, we propose the task of translating Bilingual Multi-Speaker Conversations, and explore neural architectures which exploit both source and target-side conversation histories for this task. To initiate an evaluation for this task, we introduce datasets extracted from Europarl v7 and OpenSubtitles2016. Our experiments on four language-pairs confirm the significance of leveraging conversation history, both in terms of BLEU and manual evaluation.- Anthology ID:
- W18-6311
- Volume:
- Proceedings of the Third Conference on Machine Translation: Research Papers
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 101–112
- Language:
- URL:
- https://aclanthology.org/W18-6311
- DOI:
- 10.18653/v1/W18-6311
- Cite (ACL):
- Sameen Maruf, André F. T. Martins, and Gholamreza Haffari. 2018. Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 101–112, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations (Maruf et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/author-url/W18-6311.pdf
- Code
- sameenmaruf/Bi-MSMT
- Data
- OpenSubtitles