Answer Interaction in Non-factoid Question Answering Systems
This addresses the problem of improving user interaction in answer retrieval systems, though it is incremental as it provides a basis for further research.
The study investigated how people interact with answer texts in non-factoid question answering systems, finding that users perceive and react differently to good and bad answers and can identify good answers quickly.
Information retrieval systems are evolving from document retrieval to answer retrieval. Web search logs provide large amounts of data about how people interact with ranked lists of documents, but very little is known about interaction with answer texts. In this paper, we use Amazon Mechanical Turk to investigate three answer presentation and interaction approaches in a non-factoid question answering setting. We find that people perceive and react to good and bad answers very differently, and can identify good answers relatively quickly. Our results provide the basis for further investigation of effective answer interaction and feedback methods.