Papers › ReQA: An Evaluation for End-to-End Answer Retrieval Models

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

10 Jul 2019WS 2019 11arXiv:1907.04780archive 2025-07-28

Amin Ahmad, Noah Constant, Yinfei Yang, Daniel Cer

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.

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