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        <title>Simple and Effective Semi-Supervised Question Answering</title>
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        <namePart type="given">Bhuwan</namePart>
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            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)</title>
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    <abstract>Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora. However, large domain specific annotated corpora are limited and expensive to construct. In this work, we envision a system where the end user specifies a set of base documents and only a few labelled examples. Our system exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further fine-tunes the model on the labeled examples. We evaluate our proposed system across three diverse datasets from different domains, and find it to be highly effective with very little labeled data. We attain more than 50% F1 score on SQuAD and TriviaQA with less than a thousand labelled examples. We are also releasing a set of 3.2M cloze-style questions for practitioners to use while building QA systems.</abstract>
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    <identifier type="doi">10.18653/v1/N18-2092</identifier>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Simple and Effective Semi-Supervised Question Answering
%A Dhingra, Bhuwan
%A Pruthi, Danish
%A Rajagopal, Dheeraj
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F dhingra-etal-2018-simple
%X Recent success of deep learning models for the task of extractive Question Answering (QA) is hinged on the availability of large annotated corpora. However, large domain specific annotated corpora are limited and expensive to construct. In this work, we envision a system where the end user specifies a set of base documents and only a few labelled examples. Our system exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further fine-tunes the model on the labeled examples. We evaluate our proposed system across three diverse datasets from different domains, and find it to be highly effective with very little labeled data. We attain more than 50% F1 score on SQuAD and TriviaQA with less than a thousand labelled examples. We are also releasing a set of 3.2M cloze-style questions for practitioners to use while building QA systems.
%R 10.18653/v1/N18-2092
%U https://aclanthology.org/N18-2092
%U https://doi.org/10.18653/v1/N18-2092
%P 582-587

Markdown (Informal)

[Simple and Effective Semi-Supervised Question Answering](https://aclanthology.org/N18-2092) (Dhingra et al., NAACL 2018)

• Simple and Effective Semi-Supervised Question Answering (Dhingra et al., NAACL 2018)

ACL

• Bhuwan Dhingra, Danish Pruthi, and Dheeraj Rajagopal. 2018. Simple and Effective Semi-Supervised Question Answering. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 582–587, New Orleans, Louisiana. Association for Computational Linguistics.