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Learning Personas from Dialogue with Attentive Memory Networks
Name
D18-1284.pdf
Description
Published version
Size
1.23 MB
Format
Adobe PDF
Checksum (MD5)
3a4cc67d73111c2065e02861c711cbb7
Author(s)
Chu, Eric
Vijayaraghavan, Prashanth
Roy, Deb
Date Issued
2018
Journal
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
Publisher
Association for Computational Linguistics (ACL)
Citation
Chu, Eric, Vijayaraghavan, Prashanth and Roy, Deb. 2018. "Learning Personas from Dialogue with Attentive Memory Networks." Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018.
Version
Final published version
Abstract
© 2018 Association for Computational Linguistics 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.
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
10.18653/V1/D18-1284
