Neural Machine Translation by Jointly Learning to Align and Translate

TL;DR

This paper introduces a neural machine translation model that jointly learns to align and translate, overcoming fixed-length bottlenecks and achieving performance comparable to state-of-the-art phrase-based systems.

Contribution

It proposes a novel attention-based mechanism allowing the model to focus on relevant source parts dynamically during translation.

Findings

Achieves translation quality comparable to state-of-the-art systems

Alignments learned by the model agree with human intuition

Improves over basic encoder-decoder models

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.