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

arXiv: https://arxiv.org/abs/1804.07726 · HTML: https://mlsanity.com/paper/1804.07726/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 23 of 23)

- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [The Benchmark Lottery](https://mlsanity.com/paper/2107.07002/) (2021)
- [The Generative AI Paradox: "What It Can Create, It May Not Understand"](https://mlsanity.com/paper/2311.00059/) (2023)
- [To Code, or Not To Code? Exploring Impact of Code in Pre-training](https://mlsanity.com/paper/2408.10914/) (2024)
- [Learning to Retrieve Passages without Supervision](https://mlsanity.com/paper/2112.07708/) (2021)
- [Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration](https://mlsanity.com/paper/2109.06304/) (2021)
- [Nonparametric Masked Language Modeling](https://mlsanity.com/paper/2212.01349/) (2022)
- [Phrase Retrieval Learns Passage Retrieval, Too](https://mlsanity.com/paper/2109.08133/) (2021)
- [Copy Is All You Need](https://mlsanity.com/paper/2307.06962/) (2023)
- [Question Answering Infused Pre-training of General-Purpose Contextualized Representations](https://mlsanity.com/paper/2106.08190/) (2021)
- [Reasoning Over Virtual Knowledge Bases With Open Predicate Relations](https://mlsanity.com/paper/2102.07043/) (2021)
- [Evaluating Embedding APIs for Information Retrieval](https://mlsanity.com/paper/2305.06300/) (2023)
- [MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network](https://mlsanity.com/paper/2106.07352/) (2021)
- [QA Is the New KR: Question-Answer Pairs as Knowledge Bases](https://mlsanity.com/paper/2207.00630/) (2022)
- [Two-Step Question Retrieval for Open-Domain QA](https://mlsanity.com/paper/2205.09393/) (2022)
- [Simple and Efficient ways to Improve REALM](https://mlsanity.com/paper/2104.08710/) (2021)
- [EfficientQA : a RoBERTa Based Phrase-Indexed Question-Answering System](https://mlsanity.com/paper/2101.02157/) (2021)
- [Generative Context Pair Selection for Multi-hop Question Answering](https://mlsanity.com/paper/2104.08744/) (2021)
- [Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform](https://mlsanity.com/paper/2506.09452/) (2025)
- [Bridging the Training-Inference Gap for Dense Phrase Retrieval](https://mlsanity.com/paper/2210.13678/) (2022)
- [Cross-lingual Contextualized Phrase Retrieval](https://mlsanity.com/paper/2403.16820/) (2024)
- [1-Bit Wonder: Improving QAT Performance in the Low-Bit Regime through K-Means Quantization](https://mlsanity.com/paper/2602.15563/) (2026)

## Abstract (arXiv)

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