@inproceedings{debnath-etal-2024-emo,
title = "Emo-Gen {BART} - A Multitask Emotion-Informed Dialogue Generation Framework",
author = "Debnath, Alok and
Graham, Yvette and
Conlan, Owen",
editor = "Graham, Yvette and
Liu, Qun and
Lampouras, Gerasimos and
Iacobacci, Ignacio and
Madden, Sinead and
Khalid, Haider and
Qureshi, Rameez",
booktitle = "Proceedings of the 1st Workshop on Simulating Conversational Intelligence in Chat (SCI-CHAT 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.scichat-1.7",
pages = "70--74",
abstract = "This paper is the model description for the Emo-Gen BART dialogue generation architecture, as submitted to the SCI-CHAT 2024 Shared Task. The Emotion-Informed Dialogue Generation model is a multi-task BARTbased model which performs dimensional and categorical emotion detection and uses that information to augment the input to the generation models. Our implementation is trained and validated against the IEMOCAP dataset, and compared against contemporary architectures in both dialogue emotion classification and dialogue generation. We show that certain loss function ablations are competitive against the state-of-the-art single-task models.",
}
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<abstract>This paper is the model description for the Emo-Gen BART dialogue generation architecture, as submitted to the SCI-CHAT 2024 Shared Task. The Emotion-Informed Dialogue Generation model is a multi-task BARTbased model which performs dimensional and categorical emotion detection and uses that information to augment the input to the generation models. Our implementation is trained and validated against the IEMOCAP dataset, and compared against contemporary architectures in both dialogue emotion classification and dialogue generation. We show that certain loss function ablations are competitive against the state-of-the-art single-task models.</abstract>
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%0 Conference Proceedings
%T Emo-Gen BART - A Multitask Emotion-Informed Dialogue Generation Framework
%A Debnath, Alok
%A Graham, Yvette
%A Conlan, Owen
%Y Graham, Yvette
%Y Liu, Qun
%Y Lampouras, Gerasimos
%Y Iacobacci, Ignacio
%Y Madden, Sinead
%Y Khalid, Haider
%Y Qureshi, Rameez
%S Proceedings of the 1st Workshop on Simulating Conversational Intelligence in Chat (SCI-CHAT 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F debnath-etal-2024-emo
%X This paper is the model description for the Emo-Gen BART dialogue generation architecture, as submitted to the SCI-CHAT 2024 Shared Task. The Emotion-Informed Dialogue Generation model is a multi-task BARTbased model which performs dimensional and categorical emotion detection and uses that information to augment the input to the generation models. Our implementation is trained and validated against the IEMOCAP dataset, and compared against contemporary architectures in both dialogue emotion classification and dialogue generation. We show that certain loss function ablations are competitive against the state-of-the-art single-task models.
%U https://aclanthology.org/2024.scichat-1.7
%P 70-74
Markdown (Informal)
[Emo-Gen BART - A Multitask Emotion-Informed Dialogue Generation Framework](https://aclanthology.org/2024.scichat-1.7) (Debnath et al., SCI-CHAT-WS 2024)
ACL