CMU QA

We describe a competitive question generation and answering project used in our undergraduate natural language processing courses. This semester-long project challenges teams of three or four students to use available NLP components (or develop their own) to construct systems that ask and answer questions about an arbitrary Wikipedia article. We describe how the project and competition were structured, the outcomes, and lessons learned. The Question/Answer dataset generated by students who took undergraduate natural language processing courses taught by Noah Smith at Carnegie Mellon and Rebecca Hwa at the University of Pittsburgh during Spring 2008, Spring 2009, and Spring 2010. The project proceeded in 4 phases of a 15-week semester: data preparation (weeks 1¨C4), during which the first few course lectures introduced the most important concepts for getting started in NLP and motivating applications; system development (weeks 5¨C12), during which teams worked on their systems as they learned more about problems and solutions in NLP; evaluation/competition (weeks 13¨C14); and live demonstrations (hosted by the local Google office) at the end. The first and third phases are most relevant.

本文介绍了一项用于本科自然语言处理（Natural Language Processing，NLP）课程的竞赛式问答生成项目。该项目为期一学期，要求由3至4名学生组成的团队利用现有自然语言处理组件（或自主开发相关组件），构建可针对任意维基百科（Wikipedia）文章生成问题并完成作答的系统。本文还将阐述该项目与竞赛的组织架构、最终成果及经验总结。本问答（Question/Answer）数据集由2008年春季、2009年春季与2010年春季期间，修读由卡内基梅隆大学（Carnegie Mellon University）Noah Smith与匹兹堡大学（University of Pittsburgh）Rebecca Hwa讲授的本科自然语言处理课程的学生共同生成。该项目在15周的学期中分为四个阶段：第一阶段为数据准备（第1至4周），此阶段的前几节课程将介绍入门NLP与驱动相关应用的核心概念；第二阶段为系统开发（第5至12周），团队在深入学习NLP领域的问题与解决方案的同时推进自身系统的开发；第三阶段为评估与竞赛（第13至14周）；最后为线下演示环节，由当地谷歌（Google）办公室承办。其中第一与第三阶段与本数据集的关联性最强。

搜集汇总

背景与挑战

背景概述

CMU QA是一个问答数据集，由卡内基梅隆大学和匹兹堡大学的本科生在自然语言处理课程中创建，涉及从维基百科文章生成和回答问题。数据集生成于2008年至2010年，文件大小为7.87M，属于文本类数据。