Automatic long sentence segmentation for neural machine translation

Abstract

Neural machine translation (NMT) is an emerging machine translation paradigm that translates texts with an encoder-decoder neural architecture. Very recent studies find that translation quality drops significantly when NMT translates long sentences. In this paper, we propose a novel method to deal with this issue by segmenting long sentences into several clauses. We introduce a split and reordering model to collectively detect the optimal sequence of segmentation points for a long source sentence. Each segmented clause is translated by the NMT system independently into a target clause. The translated target clauses are then concatenated without reordering to form the final translation for the long sentence. On NIST Chinese-English translation tasks, our segmentation method achieves a substantial improvement of 2.94 BLEU points over the NMT baseline on translating long sentences with more than 30 words, and 5.43 BLEU points on sentences of over 40 words.