Context-aware Neural Machine Translation with Coreference Information
Takumi Ohtani | Hidetaka Kamigaito | Masaaki Nagata | Manabu Okumura
Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)
We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly. The graph-based encoder can dynamically encode the source text without attending to all tokens in the text. In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set. Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.