Code for "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval" (ACL 2021, Long)

This is the repository for baseline models and annotated data for this paper: Akari Asai and Eunsol Choi. Challenges in Information Seeking QA:Unanswerable Questions and Paragraph Retrieval. In: Proceedings of ACL. 2021

In the paper, we carefully analyze unanswerable questions in information-seeking QA dataset (i.e., Natural Questions and TyDi QA) and attempt to identify the remaining headrooms. We conduct both a range of controlled experiments and insensitive human annotations on around 800 examples across across 6 languages.

Annotated data

In human_annotated_data, we provide human annotated data from TyDi QA and Natural Questions.

Baselines

In this work, we conduct several baseline experiments to identify the remaining headrooms in information-seeking QA. This repository include baselines for question only baseline. See the training and evaluation details in README.md. We thank the authors of Riki Net, Retro-reader, and ETC for providing their models' predictions that are used to analyze those state-of-the-art models behaviors.

Citation and Contact

If you find this codebase is useful or use in your work, please cite our paper.

@inproceedings{
asai2020learning,
title={Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval},
author={Akari Asai and Eunsol Choi},
booktitle={ACL-IJCNLP},
year={2021}
}

Please contact Akari Asai (@AkariAsai, akari[at]cs.washington.edu) for questions and suggestions.