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        <title>Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings</title>
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        <namePart type="given">Shafiq</namePart>
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    <name type="personal">
        <namePart type="given">Lluís</namePart>
        <namePart type="family">Màrquez</namePart>
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    <name type="personal">
        <namePart type="given">Preslav</namePart>
        <namePart type="family">Nakov</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>We address jointly two important tasks for Question Answering in community forums: given a new question, (i) find related existing questions, and (ii) find relevant answers to this new question. We further use an auxiliary task to complement the previous two, i.e., (iii) find good answers with respect to the thread question in a question-comment thread. We use deep neural networks (DNNs) to learn meaningful task-specific embeddings, which we then incorporate into a conditional random field (CRF) model for the multitask setting, performing joint learning over a complex graph structure. While DNNs alone achieve competitive results when trained to produce the embeddings, the CRF, which makes use of the embeddings and the dependencies between the tasks, improves the results significantly and consistently across a variety of evaluation metrics, thus showing the complementarity of DNNs and structured learning.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1452</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings
%A Joty, Shafiq
%A Màrquez, Lluís
%A Nakov, Preslav
%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 joty-etal-2018-joint
%X We address jointly two important tasks for Question Answering in community forums: given a new question, (i) find related existing questions, and (ii) find relevant answers to this new question. We further use an auxiliary task to complement the previous two, i.e., (iii) find good answers with respect to the thread question in a question-comment thread. We use deep neural networks (DNNs) to learn meaningful task-specific embeddings, which we then incorporate into a conditional random field (CRF) model for the multitask setting, performing joint learning over a complex graph structure. While DNNs alone achieve competitive results when trained to produce the embeddings, the CRF, which makes use of the embeddings and the dependencies between the tasks, improves the results significantly and consistently across a variety of evaluation metrics, thus showing the complementarity of DNNs and structured learning.
%R 10.18653/v1/D18-1452
%U https://aclanthology.org/D18-1452
%U https://doi.org/10.18653/v1/D18-1452
%P 4196-4207

Markdown (Informal)

[Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings](https://aclanthology.org/D18-1452) (Joty et al., EMNLP 2018)

• Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings (Joty et al., EMNLP 2018)

ACL

• Shafiq Joty, Lluís Màrquez, and Preslav Nakov. 2018. Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4196–4207, Brussels, Belgium. Association for Computational Linguistics.