Abstract
We introduce a Multi-modal Neural Machine Translation model in which a doubly-attentive decoder naturally incorporates spatial visual features obtained using pre-trained convolutional neural networks, bridging the gap between image description and translation. Our decoder learns to attend to source-language words and parts of an image independently by means of two separate attention mechanisms as it generates words in the target language. We find that our model can efficiently exploit not just back-translated in-domain multi-modal data but also large general-domain text-only MT corpora. We also report state-of-the-art results on the Multi30k data set.- Anthology ID:
- P17-1175
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1913–1924
- Language:
- URL:
- https://aclanthology.org/P17-1175
- DOI:
- 10.18653/v1/P17-1175
- Cite (ACL):
- Iacer Calixto, Qun Liu, and Nick Campbell. 2017. Doubly-Attentive Decoder for Multi-modal Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1913–1924, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Doubly-Attentive Decoder for Multi-modal Neural Machine Translation (Calixto et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/P17-1175.pdf
- Data
- Europarl, Flickr30k, Multi30K, WMT 2015