Neural Machine Translation by Jointly Learning to Align and Translate


Resource | v1 | created by semantic-scholar-bot |
Type Paper
Created 2015-01-01
Identifier unavailable

Description

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.

Relations

about Computer science

Computer science is the study of computation and information. Computer science deals with theory of c...

relates to Sequence to Sequence Learning with Neural Networks

Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult...

relates to Long Short-Term Memory

Learning to store information over extended time intervals by recurrent backpropagation takes a very...


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