论文标题
Cometkiwi：IST-Unbabel 2022提交质量估计共享任务
CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task
论文作者
论文摘要
我们介绍了IST和Unmabel对WMT 2022关于质量估计（QE）的共享任务的共同贡献。我们的团队参与了所有三个子任务：（i）句子和单词级质量预测； （ii）可解释的量化； （iii）关键错误检测。对于所有任务，我们在彗星框架之上构建，将其与OpenKiwi的预测估计器结构相连，并为其配备单词级序列标记器和解释提取器。我们的结果表明，在预处理过程中纳入参考可以改善下游任务上几种语言对的性能，并且通过句子和单词级别的目标共同培训可以进一步提高。此外，将注意力和梯度信息结合在一起被证明是提取句子级量化量化宽松模型的良好解释的首要策略。总体而言，我们的意见书在几乎所有语言对的所有三个任务中都取得了最佳的结果。
We present the joint contribution of IST and Unbabel to the WMT 2022 Shared Task on Quality Estimation (QE). Our team participated on all three subtasks: (i) Sentence and Word-level Quality Prediction; (ii) Explainable QE; and (iii) Critical Error Detection. For all tasks we build on top of the COMET framework, connecting it with the predictor-estimator architecture of OpenKiwi, and equipping it with a word-level sequence tagger and an explanation extractor. Our results suggest that incorporating references during pretraining improves performance across several language pairs on downstream tasks, and that jointly training with sentence and word-level objectives yields a further boost. Furthermore, combining attention and gradient information proved to be the top strategy for extracting good explanations of sentence-level QE models. Overall, our submissions achieved the best results for all three tasks for almost all language pairs by a considerable margin.
