# ReQA: An Evaluation for End-to-End Answer Retrieval Models

arXiv: https://arxiv.org/abs/1907.04780 · HTML: https://mlsanity.com/paper/1907.04780/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 16 of 16)

- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
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- [Large Dual Encoders Are Generalizable Retrievers](https://mlsanity.com/paper/2112.07899/) (2021)
- [Semantic Answer Similarity for Evaluating Question Answering Models](https://mlsanity.com/paper/2108.06130/) (2021)
- [A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations](https://mlsanity.com/paper/2109.04727/) (2021)
- [Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering](https://mlsanity.com/paper/2306.16713/) (2023)
- [Anatomizing Deep Learning Inference in Web Browsers](https://mlsanity.com/paper/2402.05981/) (2024)
- [Weakly-Supervised Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2103.02537/) (2021)
- [A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections](https://mlsanity.com/paper/2106.08908/) (2021)
- [Enhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval](https://mlsanity.com/paper/2206.02978/) (2022)
- [MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model](https://mlsanity.com/paper/2406.05733/) (2024)
- [K-COMP: Retrieval-Augmented Medical Domain Question Answering With Knowledge-Injected Compressor](https://mlsanity.com/paper/2501.13567/) (2025)
- [Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence](https://mlsanity.com/paper/2104.09393/) (2021)
- [Advancing Vietnamese Information Retrieval with Learning Objective and Benchmark](https://mlsanity.com/paper/2503.07470/) (2025)
- [Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines](https://mlsanity.com/paper/2106.07719/) (2021)
- [Retrieval Sensitivity to Identity Signals in Queries](https://mlsanity.com/paper/2609.36534/) (2026)

## Abstract (arXiv)

Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging problem, and places different requirements on the model architecture. There is growing interest in developing scalable answer retrieval models trained end-to-end, bypassing the typical document retrieval step. In this paper, we introduce Retrieval Question-Answering (ReQA), a benchmark for evaluating large-scale sentence-level answer retrieval models. We establish baselines using both neural encoding models as well as classical information retrieval techniques. We release our evaluation code to encourage further work on this challenging task.