@inproceedings{kambhatla-etal-2023-learning,
    title = "Learning Nearest Neighbour Informed Latent Word Embeddings to Improve Zero-Shot Machine Translation",
    author = "Kambhatla, Nishant  and
      Born, Logan  and
      Sarkar, Anoop",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.iwslt-1.27",
    doi = "10.18653/v1/2023.iwslt-1.27",
    pages = "291--301",
    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.",
}

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.
Kambhatla, Nishant Born, Logan Sarkar, Anoop
Learning Nearest Neighbour Informed Latent Word Embeddings to Improve Zero-Shot Machine Translation