Code-Switching for Enhancing NMT with Pre-Specified Translation

By Heng Yu, Weihua Luo, Kun Wang, Min Zhang. 2019

134 citations recorded by the source corpus.

Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Extensive experiments show that our method achieves consistent improvements over existing approaches, improving translation of constrained words without hurting unconstrained words.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, methodology): “[33, 70, 146, 171, 177, 214] and [213] use constrained decoding to incorporate specific terminology into MT, but the above methods can be repurposed to mitigate hallucinations.”
A Survey of Multilingual Neural Machine Translation (2019, methodology): “In the related sub-area of pre-training using MASS [138], mBERT/XLM [32] it was shown that using language or task tags is sufficient for distinguishing between the languages used for pre-training large models.”
Rule-based Morphological Inflection Improves Neural Terminology Translation (2021, methodology): “synthetic parallel data augmented with constraint terms on the source side (Song et al., 2019; Dinu et al., 2019).”
Lexically Constrained Neural Machine Translation with Levenshtein Transformer (2020, methodology): “A line of previous work that tried to address this problem required re-training the NMT models with lexical constraints, either by a placeholder mechanism (Crego et al., 2016) or via code-mixed training (Song et al., 2019; Dinu et al., 2019).”
EDITOR: An Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints (2020, methodology): “Lexical constraints have been incorporated in prior work via 1) constrained training where NMT models are trained on parallel samples augmented with constraint target phrases in both the source and target sequences (Song et al., 2019; Dinu et al., 2019), or 2) constrained decoding where beam search ”