Abstract
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. In experiments, we first show that our model compares favorably with state-of-the-art sequential and pipelined syntax-based NMT models. We also show that the performance of our model can be further improved by pre-training it with a small amount of treebank annotations. Our final ensemble model significantly outperforms the previous best models on the standard English-to-Japanese translation dataset.- Anthology ID:
- D17-1012
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 125–135
- Language:
- URL:
- https://aclanthology.org/D17-1012
- DOI:
- 10.18653/v1/D17-1012
- Cite (ACL):
- Kazuma Hashimoto and Yoshimasa Tsuruoka. 2017. Neural Machine Translation with Source-Side Latent Graph Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 125–135, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation with Source-Side Latent Graph Parsing (Hashimoto & Tsuruoka, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/D17-1012.pdf