Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
Minjoon Seo, Tom Kwiatkowski, Ankur Parikh, Ali Farhadi, Hannaneh Hajishirzi
Abstract
We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder. This formulation addresses a key challenge in machine comprehension by building a standalone representation of the document discourse. It additionally leads to a significant scalability advantage since the encoding of the answer candidate phrases in the document can be pre-computed and indexed offline for efficient retrieval. We experiment with baseline models for the new task, which achieve a reasonable accuracy but significantly underperform unconstrained QA models. We invite the QA research community to engage in Phrase-Indexed Question Answering (PIQA, pika) for closing the gap. The leaderboard is at: nlp.cs.washington.edu/piqa- Anthology ID:
- D18-1052
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 559–564
- Language:
- URL:
- https://aclanthology.org/D18-1052
- DOI:
- 10.18653/v1/D18-1052
- Cite (ACL):
- Minjoon Seo, Tom Kwiatkowski, Ankur Parikh, Ali Farhadi, and Hannaneh Hajishirzi. 2018. Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 559–564, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension (Seo et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/D18-1052.pdf
- Code
- uwnlp/piqa