Neural Machine Translation Quality and Post-Editing Performance

TL;DR

This study investigates how high-quality neural machine translation impacts post-editing effort and output quality, revealing that better systems reduce editing changes but BLEU scores are unreliable predictors of editing time.

Contribution

It provides empirical evidence on the relationship between neural MT quality and post-editing performance, highlighting limitations of BLEU as a predictor.

Findings

Higher NMT quality reduces post-editing changes.

BLEU scores do not reliably predict editing time.

Better NMT systems lead to improved final output quality.

Abstract

We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.