@inproceedings{926ca6dbc78e4465a12ac9091eb8dd08,
title = "An Improved Multi-task Approach to Pre-trained Model Based MT Quality Estimation",
abstract = "Machine translation (MT) quality estimation (QE) aims to automatically predict the quality of MT outputs without any references. State-of-the-art solutions are mostly fine-tuned with a pre-trained model in a multi-task framework (i.e., joint training sentence-level QE and word-level QE). In this paper, we propose an alternative multi-task framework in which post-editing results are utilized for sentence-level QE over an mBART-based encoder-decoder model. We show that the post-editing sub-task is much more in-formative and the mBART is superior to other pre-trained models. Experiments on WMT2021 English-German and English-Chinese QE datasets showed that the proposed method achieves 1.2\%–2.1\% improvements in the strong sentence-level QE baseline.",
keywords = "Multitask learning, Quality estimation, mBART",
author = "Binhuan Yuan and Yueyang Li and Kehai Chen and Hao Lu and Muyun Yang and Hailong Cao",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 18th China Conference on Machine Translation, CCMT 2022 ; Conference date: 06-08-2022 Through 10-08-2022",
year = "2022",
doi = "10.1007/978-981-19-7960-6\_11",
language = "英语",
isbn = "9789811979590",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "106--116",
editor = "Tong Xiao and Juan Pino",
booktitle = "Machine Translation - 18th China Conference, CCMT 2022, Revised Selected Papers",
address = "德国",
}