Year: 2019
Type: conference-paper
Abstract: 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.
DOI: 10.18653/v1/d19-6505
Institutions: Tokyo Institute of Technology, NTT (Japan)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2985010700