Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing

Brian Thompson,Matt Post

doi:10.18653/v1/2020.emnlp-main.8

Abstract

We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference. We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech). This results in the paraphraser’s output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and does not require human judgements for training. Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data). We also explore using our model for the task of quality estimation as a metric—conditioning on the source instead of the reference—and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.

Highlights

IntroductionMachine Translation (MT) systems have improved dramatically in the past several years
Machine Translation (MT) systems have improved dramatically in the past several years. This is largely due to advances in neural MT (NMT) methods, but the pace of improvement would not have been possible without automatic MT metrics, which provide immediate feedback on MT quality without the time and expense associated with obtaining human judgments of MT output
In en–de and zh–en, two language pairs where strong NMT systems were especially problematic for MT metrics, the Prism model is 6.8 and 19.2 BLEU points behind the strongest WMT systems, respectively

Summary

Introduction

Machine Translation (MT) systems have improved dramatically in the past several years. This is largely due to advances in neural MT (NMT) methods, but the pace of improvement would not have been possible without automatic MT metrics, which provide immediate feedback on MT quality without the time and expense associated with obtaining human judgments of MT output. The improvements that existing automatic metrics helped enable are causing the correlation between human judgments and automatic metrics to break down (Ma et al, 2019; Mathur et al, 2020) especially for BLEU (Papineni et al, 2002), which has been the de facto standard. TRAINING: LanguageAgnostic Representation Ciao amico Salut l’ami Salut.

Methods

Results

Discussion

Conclusion