Towards String-to-tree Neural Machine Translation
Roee Aharoni, Yoav Goldberg . Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2017 – 150 citations
We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.