论文标题
以多跳回答案的推理提出复杂的问题
Asking Complex Questions with Multi-hop Answer-focused Reasoning
论文作者
论文摘要
最近从自然语言文本中提出问题引起了越来越多的关注，并且提出了一些有希望的结果，提出了一些方案，并通过提出正确的疑问单词并从输入中复制相关单词到问题。但是，大多数最先进的方法都致力于提出涉及单跳关系的简单问题。在本文中，我们提出了一项名为多ihop问题生成的新任务，该任务通过额外发现和建模多种文档和相应的答案1来提出复杂且具有语义相关的问题，并建模多个实体及其语义关系1。解决问题，我们提出了多跳答答的态度，包括基于答案的单词和不同的语言信息，包括不同的粒度和包括不同的肉芽范围 - 他们的语义关系。通过对HotPotQA数据集的广泛实验，我们证明了我们提出的模型的优势和有效性，这是激励未来工作的基准。
Asking questions from natural language text has attracted increasing attention recently, and several schemes have been proposed with promising results by asking the right question words and copy relevant words from the input to the question. However, most state-of-the-art methods focus on asking simple questions involving single-hop relations. In this paper, we propose a new task called multihop question generation that asks complex and semantically relevant questions by additionally discovering and modeling the multiple entities and their semantic relations given a collection of documents and the corresponding answer 1. To solve the problem, we propose multi-hop answer-focused reasoning on the grounded answer-centric entity graph to include different granularity levels of semantic information including the word-level and document-level semantics of the entities and their semantic relations. Through extensive experiments on the HOTPOTQA dataset, we demonstrate the superiority and effectiveness of our proposed model that serves as a baseline to motivate future work.
