@inproceedings{saha-etal-2022-meta,
title = "Meta-Learning based Deferred Optimisation for Sentiment and Emotion aware Multi-modal Dialogue Act Classification",
author = "Saha, Tulika and
Patra, Aditya Prakash and
Saha, Sriparna and
Bhattacharyya, Pushpak",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.71",
doi = "10.18653/v1/2022.aacl-main.71",
pages = "978--990",
abstract = "Dialogue Act Classification (DAC) that determines the communicative intention of an utterance has been investigated widely over the years as a standalone task. But the emotional state of the speaker has a considerable effect on its pragmatic content. Sentiment as a human behavior is also closely related to emotion and one aids in the better understanding of the other. Thus, their role in identification of DAs needs to be explored. As a first step, we extend the newly released multi-modal \textit{EMOTyDA} dataset to enclose sentiment tags for each utterance. In order to incorporate these multiple aspects, we propose a Dual Attention Mechanism (DAM) based multi-modal, multi-tasking conversational framework. The DAM module encompasses intra-modal and interactive inter-modal attentions with multiple loss optimization at various hierarchies to fuse multiple modalities efficiently and learn generalized features across all the tasks. Additionally, to counter the class-imbalance issue in dialogues, we introduce a 2-step Deferred Optimisation Schedule (DOS) that involves Meta-Net (MN) learning and deferred re-weighting where the former helps to learn an explicit weighting function from data automatically and the latter deploys a re-weighted multi-task loss with a smaller learning rate. Empirically, we establish that the joint optimisation of multi-modal DAC, SA and ER tasks along with the incorporation of 2-step DOS and MN learning produces better results compared to its different counterparts and outperforms state-of-the-art model.",
}
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<abstract>Dialogue Act Classification (DAC) that determines the communicative intention of an utterance has been investigated widely over the years as a standalone task. But the emotional state of the speaker has a considerable effect on its pragmatic content. Sentiment as a human behavior is also closely related to emotion and one aids in the better understanding of the other. Thus, their role in identification of DAs needs to be explored. As a first step, we extend the newly released multi-modal EMOTyDA dataset to enclose sentiment tags for each utterance. In order to incorporate these multiple aspects, we propose a Dual Attention Mechanism (DAM) based multi-modal, multi-tasking conversational framework. The DAM module encompasses intra-modal and interactive inter-modal attentions with multiple loss optimization at various hierarchies to fuse multiple modalities efficiently and learn generalized features across all the tasks. Additionally, to counter the class-imbalance issue in dialogues, we introduce a 2-step Deferred Optimisation Schedule (DOS) that involves Meta-Net (MN) learning and deferred re-weighting where the former helps to learn an explicit weighting function from data automatically and the latter deploys a re-weighted multi-task loss with a smaller learning rate. Empirically, we establish that the joint optimisation of multi-modal DAC, SA and ER tasks along with the incorporation of 2-step DOS and MN learning produces better results compared to its different counterparts and outperforms state-of-the-art model.</abstract>
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%0 Conference Proceedings
%T Meta-Learning based Deferred Optimisation for Sentiment and Emotion aware Multi-modal Dialogue Act Classification
%A Saha, Tulika
%A Patra, Aditya Prakash
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F saha-etal-2022-meta
%X Dialogue Act Classification (DAC) that determines the communicative intention of an utterance has been investigated widely over the years as a standalone task. But the emotional state of the speaker has a considerable effect on its pragmatic content. Sentiment as a human behavior is also closely related to emotion and one aids in the better understanding of the other. Thus, their role in identification of DAs needs to be explored. As a first step, we extend the newly released multi-modal EMOTyDA dataset to enclose sentiment tags for each utterance. In order to incorporate these multiple aspects, we propose a Dual Attention Mechanism (DAM) based multi-modal, multi-tasking conversational framework. The DAM module encompasses intra-modal and interactive inter-modal attentions with multiple loss optimization at various hierarchies to fuse multiple modalities efficiently and learn generalized features across all the tasks. Additionally, to counter the class-imbalance issue in dialogues, we introduce a 2-step Deferred Optimisation Schedule (DOS) that involves Meta-Net (MN) learning and deferred re-weighting where the former helps to learn an explicit weighting function from data automatically and the latter deploys a re-weighted multi-task loss with a smaller learning rate. Empirically, we establish that the joint optimisation of multi-modal DAC, SA and ER tasks along with the incorporation of 2-step DOS and MN learning produces better results compared to its different counterparts and outperforms state-of-the-art model.
%R 10.18653/v1/2022.aacl-main.71
%U https://aclanthology.org/2022.aacl-main.71
%U https://doi.org/10.18653/v1/2022.aacl-main.71
%P 978-990
Markdown (Informal)
[Meta-Learning based Deferred Optimisation for Sentiment and Emotion aware Multi-modal Dialogue Act Classification](https://aclanthology.org/2022.aacl-main.71) (Saha et al., AACL-IJCNLP 2022)
ACL
