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    <titleInfo>
        <title>Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs</title>
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    <name type="personal">
        <namePart type="given">Minh</namePart>
        <namePart type="family">Nguyen</namePart>
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        <namePart type="given">Huu</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
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    <abstract>Finding names of people killed by police has become increasingly important as police shootings get more and more public attention (police killing detection). Unfortunately, there has been not much work in the literature addressing this problem. The early work in this field (Keith etal., 2017) proposed a distant supervision framework based on Expectation Maximization (EM) to deal with the multiple appearances of the names in documents. However, such EM-based framework cannot take full advantages of deep learning models, necessitating the use of handdesigned features to improve the detection performance. In this work, we present a novel deep learning method to solve the problem of police killing recognition. The proposed method relies on hierarchical LSTMs to model the multiple sentences that contain the person names of interests, and introduce supervised attention mechanisms based on semantical word lists and dependency trees to upweight the important contextual words. Our experiments demonstrate the benefits of the proposed model and yield the state-of-the-art performance for police killing detection.</abstract>
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        <url>https://aclanthology.org/C18-1193</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs
%A Nguyen, Minh
%A Nguyen, Thien Huu
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F nguyen-nguyen-2018-killed
%X Finding names of people killed by police has become increasingly important as police shootings get more and more public attention (police killing detection). Unfortunately, there has been not much work in the literature addressing this problem. The early work in this field (Keith etal., 2017) proposed a distant supervision framework based on Expectation Maximization (EM) to deal with the multiple appearances of the names in documents. However, such EM-based framework cannot take full advantages of deep learning models, necessitating the use of handdesigned features to improve the detection performance. In this work, we present a novel deep learning method to solve the problem of police killing recognition. The proposed method relies on hierarchical LSTMs to model the multiple sentences that contain the person names of interests, and introduce supervised attention mechanisms based on semantical word lists and dependency trees to upweight the important contextual words. Our experiments demonstrate the benefits of the proposed model and yield the state-of-the-art performance for police killing detection.
%U https://aclanthology.org/C18-1193
%P 2277-2287

Markdown (Informal)

[Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs](https://aclanthology.org/C18-1193) (Nguyen & Nguyen, COLING 2018)

• Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs (Nguyen & Nguyen, COLING 2018)

ACL

• Minh Nguyen and Thien Huu Nguyen. 2018. Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2277–2287, Santa Fe, New Mexico, USA. Association for Computational Linguistics.