Considering the widespread use of mobile and voice search, answer passage retrieval for non-factoid questions plays a critical role in modern information retrieval systems. Despite the importance of the task, the community still feels the significant lack of large-scale non-factoid question answering collections with real questions and comprehensive relevance judgments. In this paper, we develop and release a collection of 2,626 open-domain non-factoid questions from a diverse set of categories. The dataset, called ANTIQUE, contains 34,011 manual relevance annotations. The questions were asked by real users in a community question answering service, i.e., Yahoo! Answers. Relevance judgments for all the answers to each question were collected through crowdsourcing. To facilitate further research, we also include a brief analysis of the data as well as baseline results on both classical and recently developed neural IR models.
Cite
@article{arxiv.1905.08957,
title = {ANTIQUE: A Non-Factoid Question Answering Benchmark},
author = {Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and W. Bruce Croft},
journal= {arXiv preprint arXiv:1905.08957},
year = {2019}
}