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        <namePart type="given">Minjoon</namePart>
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    <name type="personal">
        <namePart type="given">Hannaneh</namePart>
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    <abstract>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 building 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</abstract>
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%0 Conference Proceedings
%T Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
%A Seo, Minjoon
%A Kwiatkowski, Tom
%A Parikh, Ankur
%A Farhadi, Ali
%A Hajishirzi, Hannaneh
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F seo-etal-2018-phrase
%X 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 building 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
%R 10.18653/v1/D18-1052
%U https://aclanthology.org/D18-1052
%U https://doi.org/10.18653/v1/D18-1052
%P 559-564

Markdown (Informal)

[Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension](https://aclanthology.org/D18-1052) (Seo et al., EMNLP 2018)

• Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension (Seo et al., EMNLP 2018)

ACL

• Minjoon Seo, Tom Kwiatkowski, Ankur Parikh, Ali Farhadi, and Hannaneh Hajishirzi. 2018. Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 559–564, Brussels, Belgium. Association for Computational Linguistics.