Document-Level Neural Machine Translation with Hierarchical Attention Networks (D18-1)
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| Challenge: | Neural machine translation (NMT) can be improved by including document-level contextual information. |
| Approach: | They propose a hierarchical attention model that captures document-level contextual information and conditioning on the NMT model’s own hidden states. |
| Outcome: | The proposed model 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. |
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