Abstract
How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpus, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality. Specifically, we propose a cross-modal contrastive learning method to learn a shared space for all languages, where both a coarse-grained sentence-level objective and a fine-grained token-level one are introduced. Experimental results and further analysis show that our method can effectively learn the cross-modal and cross-lingual alignment with a small amount of image-text pairs, and achieves significant improvements over the text-only baseline under both zero-shot and few-shot scenarios.- Anthology ID:
- 2022.emnlp-main.689
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10134–10146
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.689
- DOI:
- 10.18653/v1/2022.emnlp-main.689
- Cite (ACL):
- Zhe Yang, Qingkai Fang, and Yang Feng. 2022. Low-resource Neural Machine Translation with Cross-modal Alignment. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10134–10146, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Low-resource Neural Machine Translation with Cross-modal Alignment (Yang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.emnlp-main.689.pdf