%0 Journal Article %1 bahdanau2014neural %A Bahdanau, Dzmitry %A Cho, Kyunghyun %A Bengio, Yoshua %D 2014 %J CoRR %K attention machine paper:deep_tag_recommendation rnn translation %T Neural machine translation by jointly learning to align and translate %X Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a 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. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.

@article{bahdanau2014neural, abstract = {Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a 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. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.}, added-at = {2017-02-26T18:00:58.000+0100}, author = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua}, biburl = {https://www.bibsonomy.org/bibtex/2c07e2b0706d39522dc0d160610ac8c8b/nosebrain}, interhash = {bb2ca011eeafccb0bd2505c9476dcd10}, intrahash = {c07e2b0706d39522dc0d160610ac8c8b}, journal = {CoRR}, keywords = {attention machine paper:deep_tag_recommendation rnn translation}, timestamp = {2018-05-03T14:40:18.000+0200}, title = {Neural machine translation by jointly learning to align and translate}, year = 2014 }