Sunday, 18 October 2026 · Weekend classic from 2014 · 7 min read
Neural machine translation improves by learning soft alignments during decoding
This paper introduces a neural machine translation model that jointly learns to align source words with target words using a soft attention mechanism, achieving translation quality comparable to phrase-based systems on English-to-French tasks and better handling long sentences.
Based on the paper Neural Machine Translation by Jointly Learning to Align and Translate by Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
Challenges of fixed-length encoding in neural machine translation
Neural machine translation aims to build a single neural network that directly maps a source sentence to a target sentence, unlike traditional phrase-based systems that combine many components. Most neural approaches use an encoder-decoder architecture where the encoder compresses the source sentence into a fixed-length vector, and the decoder generates the translation from this vector.
A key difficulty is that encoding all information of a variable-length source sentence into a single fixed-length vector can be a bottleneck, especially for long sentences. Empirical evidence shows that translation quality of basic encoder-decoder models deteriorates rapidly as sentence length increases.
This limitation motivates exploring architectures that avoid compressing the entire source sentence into one vector, enabling better handling of long sentences and more flexible translation.
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