Characterizing and Supporting Question Answering in Human-to-Human Communication

Email continues to be one of the most important means of online communication. People spend a significant amount of time sending, reading, searching and responding to email in order to manage tasks, exchange information, etc. In this paper, we focus on information exchange over enterprise email in the form of questions and answers. We study a large scale publicly available email dataset to characterize information exchange via questions and answers in enterprise email. We augment our analysis with a survey to gain insights on the types of questions exchanged, when and how do people get back to them and whether this behavior is adequately supported by existing email management and search functionality. We leverage this understanding to define the task of extracting question/answer pairs from threaded email conversations. We propose a neural network based approach that matches the question to the answer considering comparisons at different levels of granularity. We also show that we can improve the performance by leveraging external data of question and answer pairs. We test our approach using a manually labeled email data collected using a crowd-sourcing annotation study. Our findings have implications for designing email clients and intelligent agents that support question answering and information lookup in email.