Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su
Abstract
Abstract Recent advancements in open-domain question answering (ODQA), that is, finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets. However, progress in QA over book stories (Book QA) lags despite its similar task formulation to ODQA. This work provides a comprehensive and quantitative analysis about the difficulty of Book QA: (1) We benchmark the research on the NarrativeQA dataset with extensive experiments with cutting-edge ODQA techniques. This quantifies the challenges Book QA poses, as well as advances the published state-of-the-art with a ∼7% absolute improvement on ROUGE-L. (2) We further analyze the detailed challenges in Book QA through human studies.1 Our findings indicate that the event-centric questions dominate this task, which exemplifies the inability of existing QA models to handle event-oriented scenarios.- Anthology ID:
- 2021.tacl-1.61
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 9
- Month:
- Year:
- 2021
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1032–1046
- Language:
- URL:
- https://aclanthology.org/2021.tacl-1.61
- DOI:
- 10.1162/tacl_a_00411
- Cite (ACL):
- Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, and Hui Su. 2021. Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study. Transactions of the Association for Computational Linguistics, 9:1032–1046.
- Cite (Informal):
- Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (Mou et al., TACL 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.tacl-1.61.pdf