Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation

TL;DR

This paper introduces an automatic segmentation method to break long sentences into manageable parts, significantly improving neural machine translation quality for lengthy inputs.

Contribution

It proposes an automatic segmentation approach to mitigate the impact of sentence length on neural translation quality, enhancing performance on long sentences.

Findings

Significant improvement in translation quality for long sentences

Effective segmentation reduces neural translation errors on lengthy inputs

Method outperforms baseline models on long sentence translation tasks

Abstract

The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically segmenting an input sentence into phrases that can be easily translated by the neural network translation model. Once each segment has been independently translated by the neural machine translation model, the translated clauses are concatenated to form a final translation. Empirical results show a significant improvement in translation quality for long sentences.