Document-Level Neural Machine Translation with Hierarchical Attention Networks

Autor:	Miculicich, Lesly, Ram, Dhananjay, Pappas, Nikolaos, Henderson, James
Rok vydání:	2018
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: EMNLP 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.01576 Zobrazit plný text záznamu View this record from Arxiv