Autores
Gelbukh Alexander
Título MIME: MIMicking emotions for empathetic response generation
Tipo Congreso
Sub-tipo Memoria
Descripción 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020
Resumen Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME. © 2020 Association for Computational Linguistics.
Observaciones
Lugar Virtual, online
País Indefinido
No. de páginas 8968-8079
Vol. / Cap.
Inicio 2020-11-16
Fin 2020-11-20
ISBN/ISSN 9781952148606