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        <title>Learning a Cost-Effective Annotation Policy for Question Answering</title>
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        <namePart type="given">Bernhard</namePart>
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        <namePart type="given">Stefan</namePart>
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    <name type="personal">
        <namePart type="given">Huan</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems is challenging. As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme. The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations. Human annotators then simply provide binary feedback on these candidates. Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost. To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost. We compare our framework against traditional manual annotations in an extensive set of experiments. We find that our approach can reduce up to 21.1% of the annotation cost.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.246</identifier>
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        <date>2020-11</date>
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%0 Conference Proceedings
%T Learning a Cost-Effective Annotation Policy for Question Answering
%A Kratzwald, Bernhard
%A Feuerriegel, Stefan
%A Sun, Huan
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kratzwald-etal-2020-learning
%X State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems is challenging. As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme. The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations. Human annotators then simply provide binary feedback on these candidates. Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost. To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost. We compare our framework against traditional manual annotations in an extensive set of experiments. We find that our approach can reduce up to 21.1% of the annotation cost.
%R 10.18653/v1/2020.emnlp-main.246
%U https://aclanthology.org/2020.emnlp-main.246
%U https://doi.org/10.18653/v1/2020.emnlp-main.246
%P 3051-3062

Markdown (Informal)

[Learning a Cost-Effective Annotation Policy for Question Answering](https://aclanthology.org/2020.emnlp-main.246) (Kratzwald et al., EMNLP 2020)

• Learning a Cost-Effective Annotation Policy for Question Answering (Kratzwald et al., EMNLP 2020)

ACL

• Bernhard Kratzwald, Stefan Feuerriegel, and Huan Sun. 2020. Learning a Cost-Effective Annotation Policy for Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3051–3062, Online. Association for Computational Linguistics.