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        <title>Injecting Relational Structural Representation in Neural Networks for Question Similarity</title>
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        <namePart type="given">Antonio</namePart>
        <namePart type="family">Uva</namePart>
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    <name type="personal">
        <namePart type="given">Daniele</namePart>
        <namePart type="family">Bonadiman</namePart>
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    <name type="personal">
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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    <abstract>Effectively using full syntactic parsing information in Neural Networks (NNs) for solving relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning a model with Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs using our approach can learn more accurate models, especially after fine tuning on GS.</abstract>
    <identifier type="citekey">uva-etal-2018-injecting</identifier>
    <identifier type="doi">10.18653/v1/P18-2046</identifier>
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        <url>https://aclanthology.org/P18-2046</url>
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%0 Conference Proceedings
%T Injecting Relational Structural Representation in Neural Networks for Question Similarity
%A Uva, Antonio
%A Bonadiman, Daniele
%A Moschitti, Alessandro
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F uva-etal-2018-injecting
%X Effectively using full syntactic parsing information in Neural Networks (NNs) for solving relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning a model with Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs using our approach can learn more accurate models, especially after fine tuning on GS.
%R 10.18653/v1/P18-2046
%U https://aclanthology.org/P18-2046
%U https://doi.org/10.18653/v1/P18-2046
%P 285-291

Markdown (Informal)

[Injecting Relational Structural Representation in Neural Networks for Question Similarity](https://aclanthology.org/P18-2046) (Uva et al., ACL 2018)

• Injecting Relational Structural Representation in Neural Networks for Question Similarity (Uva et al., ACL 2018)

ACL

• Antonio Uva, Daniele Bonadiman, and Alessandro Moschitti. 2018. Injecting Relational Structural Representation in Neural Networks for Question Similarity. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 285–291, Melbourne, Australia. Association for Computational Linguistics.