Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories

Lee Kezar


Abstract
Conversational agents, having the goal of natural language generation, must rely on language models which can integrate emotion into their responses. Recent projects outline models which can produce emotional sentences, but unlike human language, they tend to be restricted to one affective category out of a few. To my knowledge, none allow for the intentional coexistence of multiple emotions on the word or sentence level. Building on prior research which allows for variation in the intensity of a singular emotion, this research proposal outlines an LSTM (Long Short-Term Memory) language model which allows for variation in multiple emotions simultaneously.
Anthology ID:
P18-3020
Volume:
Proceedings of ACL 2018, Student Research Workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Vered Shwartz, Jeniya Tabassum, Rob Voigt, Wanxiang Che, Marie-Catherine de Marneffe, Malvina Nissim
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
141–145
Language:
URL:
https://aclanthology.org/P18-3020
DOI:
10.18653/v1/P18-3020
Bibkey:
Cite (ACL):
Lee Kezar. 2018. Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories. In Proceedings of ACL 2018, Student Research Workshop, pages 141–145, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories (Kezar, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-3020.pdf