Tailoring Domain Adaptation for Machine Translation Quality Estimation

TL;DR

This paper presents a domain adaptation and data augmentation approach for quality estimation in machine translation, improving performance across domains, languages, and in zero-shot scenarios.

Contribution

It introduces a method that combines domain adaptation with data augmentation to enhance QE models' generalizability and effectiveness.

Findings

Significant performance improvements across multiple language pairs.

Enhanced cross-lingual inference capabilities.

Superior results in zero-shot learning scenarios.

Abstract

While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular, high-quality labeled data is often lacking due to the high cost and effort associated with labeling such data. Aside from the data scarcity challenge, QE models should also be generalizable, i.e., they should be able to handle data from different domains, both generic and specific. To alleviate these two main issues -- data scarcity and domain mismatch -- this paper combines domain adaptation and data augmentation within a robust QE system. Our method first trains a generic QE model and then fine-tunes it on a specific domain while retaining generic knowledge. Our results show a significant improvement for all the language pairs investigated, better cross-lingual inference, and a superior performance in zero-shot learning scenarios as compared to state-of-the-art baselines.