Neural machine translation is a recently proposed approach to machine translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Machine Translation||IWSLT2015 German-English||Bi-GRU (MLE+SLE)||BLEU score||28.53||# 13|
|Machine Translation||WMT2014 English-French||RNN-search50*||BLEU score||36.15||# 19|