Domain, Translationese and Noise in Synthetic Data for Neural Machine Translation

Abstract

The quality of neural machine translation can be improved by leveraging additional monolingual resources to create synthetic training data. Source-side monolingual data can be (forward-)translated into the target language for self-training; target-side monolingual data can be back-translated. It has been widely reported that back-translation delivers superior results, but could this be due to artefacts in the test sets? We perform a case study using French-English news translation task and separate test sets based on their original languages. We show that forward translation delivers superior gains in terms of BLEU on sentences that were originally in the source language, complementing previous studies which show large improvements with back-translation on sentences that were originally in the target language. To better understand when and why forward and back-translation are effective, we study the role of domains, translationese, and noise. While translationese effects are well known to influence MT evaluation, we also find evidence that news data from different languages shows subtle domain differences, which is another explanation for varying performance on different portions of the test set. We perform additional low-resource experiments which demonstrate that forward translation is more sensitive to the quality of the initial translation system than back-translation, and tends to perform worse in low-resource settings.

References in corpus (2)

• Sequence-Level Knowledge Distillation
• Translationese in Machine Translation Evaluation

Cited by in corpus (9)

• Human-Paraphrased References Improve Neural Machine Translation
• The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21
• AR: Auto-Repair the Synthetic Data for Neural Machine Translation
• Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
• Synthesizing Monolingual Data for Neural Machine Translation
• On the Language Coverage Bias for Neural Machine Translation
• Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
• Fully Synthetic Data Improves Neural Machine Translation with Knowledge Distillation
• Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference