The Web as a Knowledge-base for Answering Complex Questions
Alon Talmor Tel-Aviv University、Jonathan Berant Tel-Aviv University
Abstract
Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, ComplexWebQuestions, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.71
中文速览
人类在回答"2008年澳网女单冠军来自哪个国家"这类复杂问题时，往往需要跨多个信息源进行推理和整合，而现有的机器阅读理解模型擅长简单问答、语义解析方法又受限于固定知识库，两者都难以很好地解决这一挑战。为此，研究者提出了一个"问题分解"框架：将复杂问题自动拆解为一系列简单子问题，依次交由搜索引擎和阅读理解模型处理，再通过交集、并集等符号操作将子答案合并为最终答案。为支撑这一框架的评估，研究者还构建了包含34,689条复杂问答对的新数据集ComplexWebQuestions，并训练了一个序列到序列模型来执行问题分解。实验表明，引入问题分解后，模型在该数据集上的precision@1从20.8提升至27.5，验证了该方法的有效性，同时也为后续结合知识库和表格推理提供了重要基础。
原文 arXiv:1803.06643;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1803.06643v1