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.
In human_annotated_data, we provide human annotated data from TyDi QA and Natural Questions.
| Dataset | language | # of annotated questions | file name |
|---|---|---|---|
| Natural Questions | English | 450 | NQ.tsv |
| TyDi QA | Bengali | 50 | TyDi-Bn.tsv |
| TyDi QA | Japanese | 100 | TyDi-Ja.tsv |
| TyDi QA | Korean | 100 | TyDi-Bn.tsv |
| TyDi QA | Russian | 50 | TyDi-Ru.tsv |
| TyDi QA | Telugu | 50 | TyDi-Te.tsv |
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.
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.