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            <title>Proceedings of the 2nd Workshop on Machine Reading for Question Answering</title>
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            <namePart type="given">Adam</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T ReQA: An Evaluation for End-to-End Answer Retrieval Models
%A Ahmad, Amin
%A Constant, Noah
%A Yang, Yinfei
%A Cer, Daniel
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ahmad-etal-2019-reqa
%X 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.
%R 10.18653/v1/D19-5819
%U https://aclanthology.org/D19-5819
%U https://doi.org/10.18653/v1/D19-5819
%P 137-146

Markdown (Informal)

[ReQA: An Evaluation for End-to-End Answer Retrieval Models](https://aclanthology.org/D19-5819) (Ahmad et al., 2019)

• ReQA: An Evaluation for End-to-End Answer Retrieval Models (Ahmad et al., 2019)

ACL

• Amin Ahmad, Noah Constant, Yinfei Yang, and Daniel Cer. 2019. ReQA: An Evaluation for End-to-End Answer Retrieval Models. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 137–146, Hong Kong, China. Association for Computational Linguistics.