Neural Machine Translation with Source-Side Latent Graph Parsing

This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural machine translation model, and thus the parser is optimized according to the translation objective. Our experiments show that our model significantly outperforms the previous best results on the standard English-to-Japanese translation dataset.