Abstract
Multilingual neural translation models exploit cross-lingual transfer to perform zero-shot translation between unseen language pairs. Past efforts to improve cross-lingual transfer have focused on aligning contextual sentence-level representations. This paper introduces three novel contributions to allow exploiting nearest neighbours at the token level during training, including: (i) an efficient, gradient-friendly way to share representations between neighboring tokens; (ii) an attentional semantic layer which extracts latent features from shared embeddings; and (iii) an agreement loss to harmonize predictions across different sentence representations. Experiments on two multilingual datasets demonstrate consistent gains in zero shot translation over strong baselines.- Anthology ID:
- 2023.iwslt-1.27
- Volume:
- Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada (in-person and online)
- Editors:
- Elizabeth Salesky, Marcello Federico, Marine Carpuat
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 291–301
- Language:
- URL:
- https://aclanthology.org/2023.iwslt-1.27
- DOI:
- 10.18653/v1/2023.iwslt-1.27
- Cite (ACL):
- Nishant Kambhatla, Logan Born, and Anoop Sarkar. 2023. Learning Nearest Neighbour Informed Latent Word Embeddings to Improve Zero-Shot Machine Translation. In Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), pages 291–301, Toronto, Canada (in-person and online). Association for Computational Linguistics.
- Cite (Informal):
- Learning Nearest Neighbour Informed Latent Word Embeddings to Improve Zero-Shot Machine Translation (Kambhatla et al., IWSLT 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.iwslt-1.27.pdf