FairytaleQA Translated：在低资源语言中实现教育性问答生成
FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages
AI总结：
本文将 FairytaleQA 机器翻译为低资源语言版本，并基于微调中等模型建立 QG 与 QA 基准，结合问答对生成案例和多维质量评估推动相关研究。
摘要：
问答（QA）数据集对于评估机器和人类的阅读理解能力都至关重要。尽管已经为此开发了许多英语数据集，但在低资源语言中仍存在明显空白。为缓解这一差距，本文介绍了 FairytaleQA 的机器翻译版本；FairytaleQA 是一个著名的 QA 数据集，旨在评估和提升幼儿的叙事理解能力。通过采用经过微调的中等规模模型，我们在翻译后的数据集上为问题生成（QG）和 QA 两类任务建立了基准。此外，我们还提供了一项案例研究，提出了一个用于生成问答对的模型，并在评估中纳入了问题良构性、可回答性、相关性和儿童适宜性等质量指标。我们的评估优先量化和描述错误案例，并为未来工作提供方向。本文有助于推动低资源语言中的 QA 与 QG 研究，提升阅读理解模型开发中的可及性与包容性。代码和数据公开发布于 github.com/bernardoleite/fairytaleqa-translated。
英文摘要：
Question Answering (QA) datasets are crucial in assessing reading comprehension skills for both machines and humans. While numerous datasets have been developed in English for this purpose, a noticeable void exists in less-resourced languages. To alleviate this gap, our paper introduces machine-translated versions of FairytaleQA, a renowned QA dataset designed to assess and enhance narrative comprehension skills in young children. By employing fine-tuned, modest-scale models, we establish benchmarks for both Question Generation (QG) and QA tasks within the translated datasets. In addition, we present a case study proposing a model for generating question-answer pairs, with an evaluation incorporating quality metrics such as question well-formedness, answerability, relevance, and children suitability. Our evaluation prioritizes quantifying and describing error cases, along with providing directions for future work. This paper contributes to the advancement of QA and QG research in less-resourced languages, promoting accessibility and inclusivity in the development of these models for reading comprehension. The code and data is publicly available at github.com/bernardoleite/fairytaleqa-translated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
