Abstract
We build a multi-source machine translation model and train it to maximize the probability of a target English string given French and German sources. Using the neural encoder-decoder framework, we explore several combination methods and report up to +4.8 Bleu increases on top of a very strong attention-based neural translation model.
Cite
CITATION STYLE
Zoph, B., & Knight, K. (2016). Multi-source neural translation. In 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference (pp. 30–34). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/n16-1004
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