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Papers

Document-Level Neural Machine Translation with Hierarchical Attention Networks

2018-09-05 · EMNLP 2018 10 · Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, James Henderson

Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model's own previous hidden states. Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.

📄 PDF Abstract BibTeX arXiv:1809.01576

Code (2)

idiap/HAN_NMT 공식 구현 pytorch
Nick-Zhao-Engr/Machine-Translation pytorch

Tasks

DecoderMachine TranslationNMTTranslation

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