Our main goal is to automatically search for relevant answers among many responses provided for a given question (Answer Selection), and search for relevant questions to reuse their existing answers (Question Retrieval).
Open-ended language communications introduce an enormous challenge in automatic understanding and modeling of human language. In particular, Community Question Answering (cQA) forums, such as Quora and Stackoverflow contain millions of open-ended questions and answers. The proliferation of questions and answers in such platforms motivates the ability to automatically find relevant questions to a new question, Question Retrieval, and relevant answers among existing answers, Answer Selection. From the semantic perspective, there are two major factors that make these tasks challenging: First, cQA forums contain open-domain and non-factoid questions and answers, resulting in high variance question and answer quality. Second, a considerable fraction of questions and answers are long and contain several dozen to several hundred words, resulting in difficulty of encoding questions and answers to be understood by computers. We have developed neural network approaches and demonstrated their effectiveness on the cQA tasks for ranking a list of questions or answers for a given question.
