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A Survey of Domain Adaptation for Neural Machine Translation

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/C18-1111.pdf

Summary

Neural machine translation achieves strong results when large parallel corpora exist. However, most language pairs and domains have little or no in-domain parallel data, so standard NMT performs badly on specialized text. Chu and Wang claim that theirs is the first broad review to cover how NMT can be adapted across domains. Domain adaptation here means improving in-domain translation by exploiting out-of-domain parallel data and in-domain monolingual text. The paper reviews the attention-based RNNsearch architecture and explains why its many parameters make NMT data-hungry. It then recaps earlier SMT adaptation work and organizes NMT techniques into two families. Data centric methods rely on in-domain monolingual, synthetic or parallel corpora. Model centric methods change the training objective, the architecture or the decoding procedure. The authors also discuss practical settings, such as inputs whose domain is unknown. They close with open research directions. As a survey, it reports no new experiments. Its value is a unified taxonomy for a field that was evolving quickly.

Key points

  • The taxonomy has two groups. Data centric methods use monolingual data, synthetic corpora (e.g., back-translation by Sennrich et al., 2016b) or out-of-domain parallel data. Model centric methods modify the training objective (e.g., fine-tuning, mixed fine-tuning), the architecture or the decoding.
  • The scarcity problem is illustrated with Chinese-English: patent-domain parallel data has about 1M sentence pairs, while spoken-language data has only about 200k.
  • SMT data selection and generation methods carry over to NMT. Because their criteria are not tied to NMT, the gains are modest.
  • For inputs of unknown domain, the paper covers retrieving similar training sentences and fine-tuning on them (Li et al., 2016). Farajian et al. (2017) extend this with similarity-based learning rate and epoch settings.
  • Proposed future directions:
    • adaptation for CNN-based and Transformer models;
    • incorporating domain dictionaries;
    • multilingual plus multi-domain adaptation;
    • adversarial methods and domain generalization.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Machine translation

Cited by

122 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 119 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{chu2018survey,
  title = {{A Survey of Domain Adaptation for Neural Machine Translation}},
  author = {Chu, Chenhui and Wang, Rui},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  year = {2018},
  url = {https://aclanthology.org/C18-1111/}
}

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