Emotion Conditioned Creative Dialog Generation

Khalid Alnajjar, Mika Hämäläinen


Abstract
We present a DialGPT based model for generating creative dialog responses that are conditioned based on one of the following emotions: anger, disgust, fear, happiness, pain, sadness and surprise. Our model is capable of producing a contextually apt response given an input sentence and a desired emotion label. Our model is capable of expressing the desired emotion with an accuracy of 0.6. The best performing emotions are neutral, fear and disgust. When measuring the strength of the expressed emotion, we find that anger, fear and disgust are expressed in the most strong fashion by the model.
Anthology ID:
2022.nlp4dh-1.20
Volume:
Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities
Month:
November
Year:
2022
Address:
Taipei, Taiwan
Venue:
NLP4DH
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
161–166
Language:
URL:
https://aclanthology.org/2022.nlp4dh-1.20
DOI:
Bibkey:
Cite (ACL):
Khalid Alnajjar and Mika Hämäläinen. 2022. Emotion Conditioned Creative Dialog Generation. In Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities, pages 161–166, Taipei, Taiwan. Association for Computational Linguistics.
Cite (Informal):
Emotion Conditioned Creative Dialog Generation (Alnajjar & Hämäläinen, NLP4DH 2022)
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PDF:
https://aclanthology.org/2022.nlp4dh-1.20.pdf