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        <title>Learning Personas from Dialogue with Attentive Memory Networks</title>
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        <namePart type="given">Eric</namePart>
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    <name type="personal">
        <namePart type="given">Prashanth</namePart>
        <namePart type="family">Vijayaraghavan</namePart>
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    <name type="personal">
        <namePart type="given">Deb</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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    <abstract>The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings. The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1284</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Learning Personas from Dialogue with Attentive Memory Networks
%A Chu, Eric
%A Vijayaraghavan, Prashanth
%A Roy, Deb
%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 chu-etal-2018-learning
%X The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned embeddings to find similar characters across different movies, and cluster movies according to the distribution of the embeddings. The use of short conversational text as input, and the ability to learn from prior knowledge using memory, suggests these methods could be applied to other domains.
%R 10.18653/v1/D18-1284
%U https://aclanthology.org/D18-1284
%U https://doi.org/10.18653/v1/D18-1284
%P 2638-2646

Markdown (Informal)

[Learning Personas from Dialogue with Attentive Memory Networks](https://aclanthology.org/D18-1284) (Chu et al., EMNLP 2018)

• Learning Personas from Dialogue with Attentive Memory Networks (Chu et al., EMNLP 2018)

ACL

• Eric Chu, Prashanth Vijayaraghavan, and Deb Roy. 2018. Learning Personas from Dialogue with Attentive Memory Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2638–2646, Brussels, Belgium. Association for Computational Linguistics.