Answer Sentence Selection (AS2) is a critical task for designing effective
retrieval-based Question Answering (QA) systems. Most advancements in AS2 focus
on English due to the scarcity of annotated datasets for other languages. This
lack of resources prevents the training of effective AS2 models in different
languages, creating a performance gap between QA systems in English and other
locales. In this paper, we introduce new high-quality datasets for AS2 in five
European languages (French, German, Italian, Portuguese, and Spanish), obtained
through supervised Automatic Machine Translation (AMT) of existing English AS2
datasets such as ASNQ, WikiQA, and TREC-QA using a Large Language Model (LLM).
We evaluated our approach and the quality of the translated datasets through
multiple experiments with different Transformer architectures. The results
indicate that our datasets are pivotal in producing robust and powerful
multilingual AS2 models, significantly contributing to closing the performance
gap between English and other languages.

通过使用大型语言模型，本研究提出了新的高质量数据集，用于五种欧洲语言的答案句子选择（AS2）任务，并通过多个实验证明这些数据集对于生成强大且跨语言的 AS2 模型至关重要，显著缩小了英语和其他语言之间的性能差距。