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- W4386848039 abstract "Document-level neural machine translation (NMT) has garnered considerable attention since the emergence of various context-aware NMT models. However, these static NMT models are trained on fixed parallel datasets, thus lacking awareness of the target document during inference. In order to alleviate this limitation, we propose a dynamic adapter-translator framework for context-aware NMT, which adapts the trained NMT model to the input document prior to translation. Specifically, the document adapter reconstructs the scrambled portion of the original document from a deliberately corrupted version, thereby reducing the performance disparity between training and inference. To achieve this, we employ an adaptation process in both the training and inference stages. Our experimental results on document-level translation benchmarks demonstrate significant enhancements in translation performance, underscoring the necessity of dynamic adaptation for context-aware translation and the efficacy of our methodologies." @default.
- W4386848039 created "2023-09-20" @default.
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- W4386848039 date "2023-01-01" @default.
- W4386848039 modified "2023-09-27" @default.
- W4386848039 title "FACT: A Dynamic Framework for Adaptive Context-Aware Translation" @default.
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- W4386848039 doi "https://doi.org/10.1007/978-981-99-6207-5_16" @default.
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