Datasets for Multilingual Answer Sentence Selection
This addresses the problem of limited multilingual resources for QA systems, enabling better performance in non-English languages, though it is incremental as it builds on existing English datasets.
The paper tackled the scarcity of annotated datasets for Answer Sentence Selection (AS2) in non-English languages by introducing new high-quality datasets for five European languages using supervised Automatic Machine Translation with an LLM, resulting in datasets that help produce robust multilingual AS2 models and close the performance gap with English.
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.