Simultaneous Machine Translation with Visual Context
Ozan Caglayan, Julia Ive, Veneta Haralampieva, Pranava Madhyastha, Loïc Barrault, Lucia Specia
Abstract
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation thus has to start with an incomplete source text, which is read progressively, creating the need for anticipation. In this paper, we seek to understand whether the addition of visual information can compensate for the missing source context. To this end, we analyse the impact of different multimodal approaches and visual features on state-of-the-art SiMT frameworks. Our results show that visual context is helpful and that visually-grounded models based on explicit object region information are much better than commonly used global features, reaching up to 3 BLEU points improvement under low latency scenarios. Our qualitative analysis illustrates cases where only the multimodal systems are able to translate correctly from English into gender-marked languages, as well as deal with differences in word order, such as adjective-noun placement between English and French.- Anthology ID:
- 2020.emnlp-main.184
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2350–2361
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.184
- DOI:
- 10.18653/v1/2020.emnlp-main.184
- Cite (ACL):
- Ozan Caglayan, Julia Ive, Veneta Haralampieva, Pranava Madhyastha, Loïc Barrault, and Lucia Specia. 2020. Simultaneous Machine Translation with Visual Context. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2350–2361, Online. Association for Computational Linguistics.
- Cite (Informal):
- Simultaneous Machine Translation with Visual Context (Caglayan et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.emnlp-main.184.pdf
- Code
- ImperialNLP/pysimt
- Data
- Flickr30k