Representing movie characters in dialogues

Mahmoud Azab, Noriyuki Kojima, Jia Deng, Rada Mihalcea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other participants in the dialogue. We evaluate the performance of these new character embeddings on two tasks: (1) character relatedness, using a dataset we introduce consisting of a dense character interaction matrix for 4,761 unique character pairs over 22 hours of dialogue from eighteen movies; and (2) character relation classification, for fine- and coarse-grained relations, as well as sentiment relations. Our experiments show that our model significantly outperforms the traditional Word2Vec continuous bag-of-words and skip-gram models, demonstrating the effectiveness of the character embeddings we introduce. We further show how these embeddings can be used in conjunction with a visual question answering system to improve over previous results.

Original languageEnglish (US)
Title of host publicationCoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference
PublisherAssociation for Computational Linguistics
Pages99-109
Number of pages11
ISBN (Electronic)9781950737727
StatePublished - 2019
Event23rd Conference on Computational Natural Language Learning, CoNLL 2019 - Hong Kong, China
Duration: Nov 3 2019Nov 4 2019

Publication series

NameCoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference

Conference

Conference23rd Conference on Computational Natural Language Learning, CoNLL 2019
CountryChina
CityHong Kong
Period11/3/1911/4/19

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Computational Theory and Mathematics

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  • Cite this

    Azab, M., Kojima, N., Deng, J., & Mihalcea, R. (2019). Representing movie characters in dialogues. In CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference (pp. 99-109). (CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference). Association for Computational Linguistics.