Context-Adaptive Document-Level Neural Machine Translation
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Abstract (arXiv)
Most existing document-level neural machine translation (NMT) models leverage a fixed number of the previous or all global source sentences to handle the context-independent problem in standard NMT. However, the translating of each source sentence benefits from various sizes of context, and inappropriate context may harm the translation performance. In this work, we introduce a data-adaptive method that enables the model to adopt the necessary and useful context. Specifically, we introduce a light predictor into two document-level translation models to select the explicit context. Experiments demonstrate the proposed approach can significantly improve the performance over the previous methods with a gain up to 1.99 BLEU points.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 16 Apr 2021.
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References · 17
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- 2020Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learningcited by 25
5 more
- 2020Deep Transformers with Latent Depthcited by 17
- 2019Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translationcited by 10
- 2020Towards Making the Most of Context in Neural Machine Translationcited by 7
- 2020Capturing document context inside sentence-level neural machine translation models with self-trainingcited by 7
- 2020Using Context in Neural Machine Translation Training Objectivescited by 7
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