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 generalizabile, 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 is to first train a generic QE model and then fine-tune 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.

With the increase in machine translation (MT) quality over the latest years, it has now become a common practice to integrate MT in the workflow of language service providers (LSPs) and other actors in the translation industry. With MT having a direct impact on the translation workflow, it is important not only to use high-quality MT systems, but also to understand the quality dimension so that the humans involved in the translation workflow can make informed decisions. The evaluation and monitoring of MT output quality has become one of the essential aspects of language technology management in LSPs’ workflows. First, a general practice is to carry out human tests to evaluate MT output quality before deployment. Second, a quality estimate of the translated text, thus after deployment, can inform post editors or even represent post-editing effort. In the former case, based on the quality assessment of a candidate engine, an informed decision can be made whether the engine would be deployed for production or not. In the latter, a quality estimate of the translation output can guide the human post-editor or even make rough approximations of the post-editing effort. Quality of an MT engine can be assessed on document- or on sentence-level. A tool to jointly provide all these functionalities does not exist yet. The overall objective of the project presented in this paper is to develop an MT quality assessment (MTQA) tool that simplifies the quality assessment of MT engines, combining quality evaluation and quality estimation on document- and sentence- level.