论文标题
多跳的问答系统是否知道如何回答单跳子问题？
Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?
论文作者
论文摘要
多跳问题回答（QA）需要一个模型来检索和整合长文本不同部分的信息以回答问题。人类通过分裂和争议的方法回答了这种复杂的问题。在本文中，我们调查了多跳问题的表现最佳模型是否了解像人类这样的基本子问题。我们采用神经分解模型来生成一个多跳复合问题的子问题，然后提取相应的子媒介。我们表明，尽管正确回答了相应的多跳问题，但多个最先进的多跳质量质量质量检查模型无法正确回答一个子问题的大部分。这表明这些模型设法使用一些部分线索来回答多跳的问题，而不是真正理解推理路径。我们还提出了一个新模型，可显着提高回答子问题的绩效。我们的工作朝着建立更容易解释的多跳质量质量检查系统迈出了一步。
Multi-hop question answering (QA) requires a model to retrieve and integrate information from different parts of a long text to answer a question. Humans answer this kind of complex questions via a divide-and-conquer approach. In this paper, we investigate whether top-performing models for multi-hop questions understand the underlying sub-questions like humans. We adopt a neural decomposition model to generate sub-questions for a multi-hop complex question, followed by extracting the corresponding sub-answers. We show that multiple state-of-the-art multi-hop QA models fail to correctly answer a large portion of sub-questions, although their corresponding multi-hop questions are correctly answered. This indicates that these models manage to answer the multi-hop questions using some partial clues, instead of truly understanding the reasoning paths. We also propose a new model which significantly improves the performance on answering the sub-questions. Our work takes a step forward towards building a more explainable multi-hop QA system.
