论文标题
开放域问使用网络表
Open Domain Question Answering Using Web Tables
论文作者
论文摘要
从Web文档中提取的表可直接回答许多Web搜索查询。使用Web表的问题回答（QA）的先前工作重点介绍了Factoid查询，即用简短的字符串（例如人名称或数字）响应的问题。但是，使用表可以回答的许多查询本质上是非事实的。在本文中，我们使用适用于FACTOIT和非事实查询的Web表开发了开放域质量检查方法。我们的主要见解是将查询和表格之间的深层神经网络的语义相似性与量化文档中表格的特征以及表格中的信息质量相结合。我们对现实Web搜索查询的实验表明，我们的方法显着优于最先进的基线方法。我们的解决方案用于主要的商业网络搜索引擎中的生产中，并为每月数千万实际的用户查询提供直接答案。
Tables extracted from web documents can be used to directly answer many web search queries. Previous works on question answering (QA) using web tables have focused on factoid queries, i.e., those answerable with a short string like person name or a number. However, many queries answerable using tables are non-factoid in nature. In this paper, we develop an open-domain QA approach using web tables that works for both factoid and non-factoid queries. Our key insight is to combine deep neural network-based semantic similarity between the query and the table with features that quantify the dominance of the table in the document as well as the quality of the information in the table. Our experiments on real-life web search queries show that our approach significantly outperforms state-of-the-art baseline approaches. Our solution is used in production in a major commercial web search engine and serves direct answers for tens of millions of real user queries per month.
