Abstract
For humans and computers, the first step in answering an open-domain question is retrieving a set of relevant documents from a large corpus. However, the strategies that computers use fundamentally differ from those of humans. To better understand these differences, we design a gamified interface for data collection—Cheater’s Bowl—where a human answers complex questions with access to both traditional and modern search tools. We collect a dataset of human search sessions, analyze human search strategies, and compare them to state-of-the-art multi-hop QA models. Humans query logically, apply dynamic search chains, and use world knowledge to boost searching. We demonstrate how human queries can improve the accuracy of existing systems and propose improving the future design of QA models.- Anthology ID:
- 2022.findings-emnlp.266
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3627–3639
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.266
- DOI:
- 10.18653/v1/2022.findings-emnlp.266
- Cite (ACL):
- Wanrong He, Andrew Mao, and Jordan Boyd-Graber. 2022. Cheater’s Bowl: Human vs. Computer Search Strategies for Open-Domain QA. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3627–3639, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Cheater’s Bowl: Human vs. Computer Search Strategies for Open-Domain QA (He et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.findings-emnlp.266.pdf