While impressive performance has been achieved on the task of Answer Sentence Selection (AS2) for English, the same does not hold for languages that lack large labeled datasets. In this work, we propose Cross-Lingual Knowledge Distillation (CLKD) from a strong English AS2 teacher as a method to train AS2 models for low-resource languages in the tasks without the need of labeled data for the target language. To evaluate our method, we introduce 1) Xtr-WikiQA, a translation-based WikiQA dataset for 9 additional languages, and 2) TyDi-AS2, a multilingual AS2 dataset with over 70K questions spanning 8 typologically diverse languages. We conduct extensive experiments on Xtr-WikiQA and TyDi-AS2 with multiple teachers, diverse monolingual and multilingual pretrained language models (PLMs) as students, and both monolingual and multilingual training. The results demonstrate that CLKD either outperforms or rivals even supervised fine-tuning with the same amount of labeled data and a combination of machine translation and the teacher model. Our method can potentially enable stronger AS2 models for low-resource languages, while TyDi-AS2 can serve as the largest multilingual AS2 dataset for further studies in the research community.

本文提出了一种名为Cross-Lingual Knowledge Distillation (CLKD)的方法，使用强大的英文Answer Sentence Selection（AS2）模型作为老师进行跨语言知识蒸馏，可用于训练对于标注数据有限的低资源语言的AS2模型。作者还介绍了两个多语言数据集进行了实证研究，通过多次实验验证，CLKD方法在不用标注数据的情况下，可与使用相同数量标注数据的监督微调方法相媲美，具有潜力为低资源语言提供更强大的AS2模型，同时这两个多语言数据集能够在AS2领域进行更广泛的研究。

低资源语言下跨语言知识蒸馏的答案句子选择