In this paper, we describe our participation in the 2021 Workshop on Asian Translation (team ID: tpt_wat). We submitted results for all six directions of the JPC2 patent task. As a first-time participant in the task, we attempted to identify a single configuration that provided the best overall results across all language pairs. All our submissions were created using single base transformer models, trained on only the task-specific data, using a consistent configuration of hyperparameters. In contrast to the uniformity of our methods, our results vary widely across the six language pairs.

This paper presents a case study of applying machine translation quality estimation (QE) for the purpose of machine translation (MT) engine selection. The goal is to understand how well the QE predictions correlate with several MT evaluation metrics (automatic and human). Our findings show that our industry-level QE system is not reliable enough for MT selection when the MT systems have similar performance. We suggest that QE can be used with more success for other tasks relevant for translation industry such as risk prevention.