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        <title>An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks</title>
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    <name type="personal">
        <namePart type="given">Anirban</namePart>
        <namePart type="family">Laha</namePart>
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    <name type="personal">
        <namePart type="given">Vikas</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Several tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification). For several single sequence classification tasks, the current state-of-the-art approaches are based on recurrent and convolutional neural networks. On the other hand, for bi-sequence classification problems, there is not much understanding as to the best deep learning architecture. In this paper, we attempt to get an understanding of this category of problems by extensive empirical evaluation of 19 different deep learning architectures (specifically on different ways of handling context) for various problems originating in natural language processing like debating, textual entailment and question-answering. Following the empirical evaluation, we offer our insights and conclusions regarding the architectures we have considered. We also establish the first deep learning baselines for three argumentation mining tasks.</abstract>
    <identifier type="citekey">laha-raykar-2016-empirical</identifier>
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        <url>https://aclanthology.org/C16-1260</url>
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        <date>2016-12</date>
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            <start>2762</start>
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%0 Conference Proceedings
%T An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks
%A Laha, Anirban
%A Raykar, Vikas
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F laha-raykar-2016-empirical
%X Several tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification). For several single sequence classification tasks, the current state-of-the-art approaches are based on recurrent and convolutional neural networks. On the other hand, for bi-sequence classification problems, there is not much understanding as to the best deep learning architecture. In this paper, we attempt to get an understanding of this category of problems by extensive empirical evaluation of 19 different deep learning architectures (specifically on different ways of handling context) for various problems originating in natural language processing like debating, textual entailment and question-answering. Following the empirical evaluation, we offer our insights and conclusions regarding the architectures we have considered. We also establish the first deep learning baselines for three argumentation mining tasks.
%U https://aclanthology.org/C16-1260
%P 2762-2773

Markdown (Informal)

[An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks](https://aclanthology.org/C16-1260) (Laha & Raykar, COLING 2016)

• An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks (Laha & Raykar, COLING 2016)

ACL

• Anirban Laha and Vikas Raykar. 2016. An Empirical Evaluation of various Deep Learning Architectures for Bi-Sequence Classification Tasks. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2762–2773, Osaka, Japan. The COLING 2016 Organizing Committee.