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Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation

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arxiv 2004.02577 v1 pith:GPS25F6J submitted 2020-04-06 cs.CL

Wei Peng , Chongxuan Huang , Tianhao Li , Yun Chen , Qun Liu This is my paper · ORCID
classification cs.CL
keywords datadomainaugmentationmodelstranslationassociatedbaselinecorpora
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Existing data augmentation approaches for neural machine translation (NMT) have predominantly relied on back-translating in-domain (IND) monolingual corpora. These methods suffer from issues associated with a domain information gap, which leads to translation errors for low frequency and out-of-vocabulary terminology. This paper proposes a dictionary-based data augmentation (DDA) method for cross-domain NMT. DDA synthesizes a domain-specific dictionary with general domain corpora to automatically generate a large-scale pseudo-IND parallel corpus. The generated pseudo-IND data can be used to enhance a general domain trained baseline. The experiments show that the DDA-enhanced NMT models demonstrate consistent significant improvements, outperforming the baseline models by 3.75-11.53 BLEU. The proposed method is also able to further improve the performance of the back-translation based and IND-finetuned NMT models. The improvement is associated with the enhanced domain coverage produced by DDA.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation

    cs.CL 2024-12 conditional novelty 5.0 of 10

    In a low-resource domain adaptation setup, the simplest dictionary-based word-substitution method (DALI) beats more complex pretraining and copying methods, nearly doubling ChrF, but absolute translation quality remains low.

  2. In-Domain African Languages Translation Using LLMs and Multi-armed Bandits

    cs.CL 2025-05 reject novelty 4.0 of 10

    Bandit-based model selection matches or slightly improves on the best single NMT system for in-domain English-to-African translation, but the claimed high-confidence statistical support is absent.

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