Real-time open-domain question answering with dense-sparse phrase index

Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive. In this paper, we introduce query-agnostic indexable representations of document phrases that can drastically speed up open-domain QA. In particular, our dense-sparse phrase encoding effectively captures syntactic, semantic, and lexical information of the phrases and eliminates the pipeline filtering of context documents. Leveraging strategies for optimizing training and inference time, our model can be trained and deployed even in a single 4-GPU server. Moreover, by representing phrases as pointers to their start and end tokens, our model indexes phrases in the entire English Wikipedia (up to 60 billion phrases) using under 2TB. Our experiments on SQuAD-Open show that our model is on par with or more accurate than previous models with 6000x reduced computational cost, which translates into at least 68x faster end-to-end inference benchmark on CPUs. Code and demo are available at nlp.

Original languageEnglish
Title of host publicationACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages4430-4441
Number of pages12
ISBN (Electronic)9781950737482
Publication statusPublished - 2020
Event57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 - Florence, Italy
Duration: 2019 Jul 282019 Aug 2

Publication series

NameACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics, ACL 2019
CountryItaly
CityFlorence
Period19/7/2819/8/2

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science(all)
  • Linguistics and Language

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  • Cite this

    Seo, M., Lee, J., Kwiatkowski, T., Parikh, A. P., Farhadi, A., & Hajishirzi, H. (2020). Real-time open-domain question answering with dense-sparse phrase index. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 4430-4441). (ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference). Association for Computational Linguistics (ACL).