论文标题
与论文交谈：带来神经问题，回答学术搜索
Talk to Papers: Bringing Neural Question Answering to Academic Search
论文作者
论文摘要
我们向论文介绍了谈话，该论文利用了最近的开放域问题答案（QA）技术来改善当前的学术搜索体验。它旨在使研究人员能够使用自然语言查询来找到精确的答案，并从大量的学术论文中提取见解。我们在几个标准质量检查数据集上对经典搜索引擎基线进行了大量改进，并为社区提供了一种协作数据收集工具，可通过社区努力来策划第一个自然语言处理QA数据集。
We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search. It's designed to enable researchers to use natural language queries to find precise answers and extract insights from a massive amount of academic papers. We present a large improvement over classic search engine baseline on several standard QA datasets and provide the community a collaborative data collection tool to curate the first natural language processing research QA dataset via a community effort.
