@inproceedings{kabir-carpuat-2021-umd,
    title = "The {UMD} Submission to the Explainable {MT} Quality Estimation Shared Task: Combining Explanation Models with Sequence Labeling",
    author = "Kabir, Tasnim  and
      Carpuat, Marine",
    editor = "Gao, Yang  and
      Eger, Steffen  and
      Zhao, Wei  and
      Lertvittayakumjorn, Piyawat  and
      Fomicheva, Marina",
    booktitle = "Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.eval4nlp-1.22/",
    doi = "10.18653/v1/2021.eval4nlp-1.22",
    pages = "230--237",
    abstract = "This paper describes the UMD submission to the Explainable Quality Estimation Shared Task at the EMNLP 2021 Workshop on {\textquotedblleft}Evaluation {\&} Comparison of NLP Systems{\textquotedblright}. We participated in the word-level and sentence-level 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."
}