# Answer Interaction in Non-factoid Question Answering Systems

arXiv: https://arxiv.org/abs/1901.03491 · HTML: https://mlsanity.com/paper/1901.03491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Towards a Better Understanding Human Reading Comprehension with Brain Signals](https://mlsanity.com/paper/2108.01360/) (2021)
- [Long-context Non-factoid Question Answering in Indic Languages](https://mlsanity.com/paper/2504.13615/) (2025)

## Abstract (arXiv)

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.