# Answer Interaction in Non-factoid Question Answering Systems

## TL;DR

This paper investigates how users interact with answer texts in non-factoid question answering systems, revealing differences in perception of answer quality and the speed of identifying good answers, based on user studies.

## Contribution

It introduces a user study on answer interaction approaches in non-factoid QA, highlighting how answer quality affects user perception and response time.

## Key findings

- Users perceive and react differently to good and bad answers.
- Good answers are identified more quickly by users.
- The study provides insights for improving answer interaction methods.

## Abstract

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.