mlsanityData to 7 Oct 2026

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

arXiv 1409.1257 ↗First posted 3 Sep 2014cs.CL cs.LG cs.NEcited by 14

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2014.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 6; 2022: 3; 2023: 1; 2024: 1; 2025: 3.

14 papers.

Sort: