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。

多语种答案句子选择数据集

Datasets for Multilingual Answer Sentence Selection

In this paper, we investigate which questions are challenging for
retrieval-based Question Answering (QA). We (i) propose retrieval complexity
(RC), a novel metric conditioned on the completeness of retrieved documents,
which measures the difficulty of answering questions, and (ii) propose an
unsupervised pipeline to measure RC given an arbitrary retrieval system. Our
proposed pipeline measures RC more accurately than alternative estimators,
including LLMs, on six challenging QA benchmarks. Further investigation reveals
that RC scores strongly correlate with both QA performance and expert judgment
across five of the six studied benchmarks, indicating that RC is an effective
measure of question difficulty. Subsequent categorization of high-RC questions
shows that they span a broad set of question shapes, including multi-hop,
compositional, and temporal QA, indicating that RC scores can categorize a new
subset of complex questions. Our system can also have a major impact on
retrieval-based systems by helping to identify more challenging questions on
existing datasets.

该研究通过提出检索复杂度（RC）指标以及一个无监督的测量 RC 的流程，较准确地衡量了检索式问答任务中问题的难度，并发现 RC 与问答系统性能和专家判断之间有很强的相关性，可以对复杂问题进行分类。这一系统还可以通过确定现有数据集中的更具挑战性的问题，对检索式系统产生重大影响。