# Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension

Source: [Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension](<https://www.sota2.com/research/paper/68fa64dc1e209131cea06cb4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:12:58.577Z

Authors: Minjoon Seo, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

Published: 2018-04-20T17:05:03.000Z

## 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 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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Question Answering on SQuAD v1.1 (test)](<https://www.sota2.com/research/sota/question-answering-on-squad-v1-1-test.md>): Question Answering; SQuAD v1.1 (test); F1 Score: 62.7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1804.07726>)

[PDF](<https://arxiv.org/pdf/1804.07726.pdf>)