CAREER: Human-Computer Cooperation for Word-by-Word Question Answering

这个CAREER项目研究人类和计算机如何一起工作来回答问题。 人类和计算机拥有互补的技能：人类对世界有着广泛的常识性理解，对非传统语言有着更强的能力，而计算机可以毫不费力地记住无数事实，并在瞬间检索出来。这个提议帮助机器理解人、地点和角色是谁;如何将这些信息传达给人类;以及如何允许人类和计算机在使用有限信息的问题回答中进行协作。 这个提议的一个关键组成部分是逐字回答问题：这迫使人类和计算机尽可能有效地使用信息来回答问题。 除了在问答任务中嵌入这些技能之外，该提案还具有广泛的推广计划，以在高中和大学生的交互式问答比赛中展示这种技术。这项研究通过在中等维度嵌入中编码实体之间关系的实体的新表示（例如，“GoodluckJonathan”和“尼日利亚”的表示编码前者是后者的领导者），以使系统能够回答关于尼日利亚的问题。 我们通过传统的问答评估和与人类合作的交互实验来验证这些表示的有效性，以确保我们可以有效地可视化这些表示。 除了帮助训练计算机回答问题外，我们还使用对手建模和强化学习来帮助训练人类更好地回答问题。

This CAREER project investigates how humans and computers can work together to answer questions. Humans and computers possess complementary skills: humans have extensive commonsense understanding of the world and greater facility with unconventional language, while computers can effortlessly memorize countless facts and retrieve them in an instant. This proposal helps machines understand who people, places, and characters are; how to communicate this information to humans; and how to allow humans and computers to collaborate in question answering using limited information. A key component of this proposal is answering questions word-by-word: this forces both humans and computers to answer questions using information as efficiently as possible. In addition to embedding these skills in question answering tasks, this proposal has an extensive outreach program to exhibit this technology in interactive question answering competitions for high school and college students.This research is possible by a new representations of entities in a medium-dimensional embedding that encodes relationships between entities (e.g., the representation of "Goodluck Jonathan" and "Nigeria" encodes that the former is the leader of the latter) to enable the system to answer questions about Nigeria. We validate the effectiveness of these representations both through traditional question answering evaluations and through interactive experiments with human collaboration to ensure that we can visualize these representations effectively. In addition to helping train computers to answer questions, we use opponent modeling and reinforcement learning to help train humans to better answer questions.