Neural Machine Translation by Jointly Learning to Align and Translate
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2014
- DOI:
- 10.48550/arXiv.1409.0473
- arXiv:
- arXiv:1409.0473
- Bibcode:
- 2014arXiv1409.0473B
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Machine Learning;
- Computer Science - Neural and Evolutionary Computing;
- Statistics - Machine Learning
- E-Print:
- Accepted at ICLR 2015 as oral presentation