The multi-answer phenomenon, where a question may have multiple answers scattered in the document, can be well handled by humans but is challenging enough for machine reading comprehension (MRC) systems. Despite recent progress in multi-answer MRC, there lacks a systematic analysis of how this phenomenon arises and how to better address it. In this work, we design a taxonomy to categorize commonly-seen multi-answer MRC instances, with which we inspect three multi-answer datasets and analyze where the multi-answer challenge comes from. We further analyze how well different paradigms of current multi-answer MRC models deal with different types of multi-answer instances. We find that some paradigms capture well the key information in the questions while others better model the relationship between questions and contexts. We thus explore strategies to make the best of the strengths of different paradigms. Experiments show that generation models can be a promising platform to incorporate different paradigms. Our annotations and code are released for further research.

翻译：多答案现象指一个问题可能在文档中分散存在多个答案，人类能很好地处理该问题，但对机器阅读理解（MRC）系统而言极具挑战性。尽管多答案MRC近期取得进展，但对该现象如何产生及如何更好地解决仍缺乏系统分析。本研究设计了一个分类体系来归类常见的多答案MRC实例，并据此检查了三个多答案数据集，分析了多答案挑战的来源。我们进一步分析了当前多答案MRC模型的不同范式如何应对不同类型的多答案实例。研究发现，部分范式能较好地捕捉问题中的关键信息，而另一些范式则能更好地建模问题与上下文之间的关系。为此，我们探索了充分利用不同范式优势的策略。实验表明，生成模型可作为融合不同范式的有前景平台。我们已公开发布标注数据和代码以供后续研究。