  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.
