Multi-Source Syntactic Neural Machine Translation
Anna Currey, Kenneth Heafield · 2018
We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses.This is achieved by employing separate encoders for the sequential and parsed versions of the same source sentence; the resulting representations are then combined using a hierarchical attention mechanism.The proposed model improves over both seq2seq and parsed baselines by over 1 BLEU on the WMT17 English→German task.Further analysis shows that our multi-source syntactic model is able to translate successfully without any parsed input, unlike standard parsed methods.In addition, performance does not deteriorate as much on long sentences as for the baselines.