Abstract
We propose approaches to Quality Estimation (QE) for Machine Translation that explore both text and visual modalities for Multimodal QE. We compare various multimodality integration and fusion strategies. For both sentence-level and document-level predictions, we show that state-of-the-art neural and feature-based QE frameworks obtain better results when using the additional modality.- Anthology ID:
- 2020.acl-main.114
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1233–1240
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.114
- DOI:
- 10.18653/v1/2020.acl-main.114
- Cite (ACL):
- Shu Okabe, Frédéric Blain, and Lucia Specia. 2020. Multimodal Quality Estimation for Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1233–1240, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multimodal Quality Estimation for Machine Translation (Okabe et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.acl-main.114.pdf