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        <title>Deriving Machine Attention from Human Rationales</title>
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        <namePart type="given">Yujia</namePart>
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        <namePart type="given">Shiyu</namePart>
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    <name type="personal">
        <namePart type="given">Regina</namePart>
        <namePart type="family">Barzilay</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectively. To this end, we start with discrete human-annotated rationales and map them into continuous attention. Our central hypothesis is that this mapping is general across domains, and thus can be transferred from resource-rich domains to low-resource ones. Our model jointly learns a domain-invariant representation and induces the desired mapping between rationales and attention. Our empirical results validate this hypothesis and show that our approach delivers significant gains over state-of-the-art baselines, yielding over 15% average error reduction on benchmark datasets.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1216</identifier>
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        <date>2018-oct-nov</date>
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            <start>1903</start>
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%0 Conference Proceedings
%T Deriving Machine Attention from Human Rationales
%A Bao, Yujia
%A Chang, Shiyu
%A Yu, Mo
%A Barzilay, Regina
%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 bao-etal-2018-deriving
%X Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectively. To this end, we start with discrete human-annotated rationales and map them into continuous attention. Our central hypothesis is that this mapping is general across domains, and thus can be transferred from resource-rich domains to low-resource ones. Our model jointly learns a domain-invariant representation and induces the desired mapping between rationales and attention. Our empirical results validate this hypothesis and show that our approach delivers significant gains over state-of-the-art baselines, yielding over 15% average error reduction on benchmark datasets.
%R 10.18653/v1/D18-1216
%U https://aclanthology.org/D18-1216
%U https://doi.org/10.18653/v1/D18-1216
%P 1903-1913

Markdown (Informal)

[Deriving Machine Attention from Human Rationales](https://aclanthology.org/D18-1216) (Bao et al., EMNLP 2018)

• Deriving Machine Attention from Human Rationales (Bao et al., EMNLP 2018)

ACL

• Yujia Bao, Shiyu Chang, Mo Yu, and Regina Barzilay. 2018. Deriving Machine Attention from Human Rationales. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1903–1913, Brussels, Belgium. Association for Computational Linguistics.