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        <title>A Genre-Aware Attention Model to Improve the Likability Prediction of Books</title>
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        <namePart type="given">Suraj</namePart>
        <namePart type="family">Maharjan</namePart>
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    <name type="personal">
        <namePart type="given">Manuel</namePart>
        <namePart type="family">Montes</namePart>
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        <namePart type="given">Fabio</namePart>
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    <name type="personal">
        <namePart type="given">Thamar</namePart>
        <namePart type="family">Solorio</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>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Julia</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>Likability prediction of books has many uses. Readers, writers, as well as the publishing industry, can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architecture that incorporates genre supervision to assign weights to individual feature types. Our proposed method is capable of dynamically tailoring weights given to feature types based on the characteristics of each book. Our architecture achieves competitive results and even outperforms state-of-the-art for this task.</abstract>
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%0 Conference Proceedings
%T A Genre-Aware Attention Model to Improve the Likability Prediction of Books
%A Maharjan, Suraj
%A Montes, Manuel
%A González, Fabio A.
%A Solorio, Thamar
%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 maharjan-etal-2018-genre
%X Likability prediction of books has many uses. Readers, writers, as well as the publishing industry, can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architecture that incorporates genre supervision to assign weights to individual feature types. Our proposed method is capable of dynamically tailoring weights given to feature types based on the characteristics of each book. Our architecture achieves competitive results and even outperforms state-of-the-art for this task.
%R 10.18653/v1/D18-1375
%U https://aclanthology.org/D18-1375
%U https://doi.org/10.18653/v1/D18-1375
%P 3381-3391

Markdown (Informal)

[A Genre-Aware Attention Model to Improve the Likability Prediction of Books](https://aclanthology.org/D18-1375) (Maharjan et al., EMNLP 2018)

• A Genre-Aware Attention Model to Improve the Likability Prediction of Books (Maharjan et al., EMNLP 2018)

ACL

• Suraj Maharjan, Manuel Montes, Fabio A. González, and Thamar Solorio. 2018. A Genre-Aware Attention Model to Improve the Likability Prediction of Books. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3381–3391, Brussels, Belgium. Association for Computational Linguistics.