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 requiring 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
@article{arxiv.1804.07726,
title = {Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension},
author = {Minjoon Seo and Tom Kwiatkowski and Ankur P. Parikh and Ali Farhadi and Hannaneh Hajishirzi},
journal= {arXiv preprint arXiv:1804.07726},
year = {2018}
}