The UMD Submission to the Explainable MT Quality Estimation Shared Task: Combining Explanation Models with Sequence Labeling
Tasnim Kabir, Marine Jacinthe Carpuat · 2021
This paper describes the UMD submission to the Explainable Quality Estimation Shared Task at the Eval4NLP 2021 Workshop on "Evaluation & Comparison of NLP Systems".We participated in the word-level and sentencelevel MT Quality Estimation (QE) constrained tasks for all language pairs: Estonian-English, Romanian-English, German-Chinese, and Russian-German.Our approach combines the predictions of a word-level explainer model on top of a sentence-level QE model and a sequence labeler trained on synthetic data.These models are based on pre-trained multilingual language models and do not require any word-level annotations for training, making them well suited to zero-shot settings.Our best performing system improves over the best baseline across all metrics and language pairs, with an average gain of 0.1 in AUC, Average Precision, and Recall at Top-K score.