DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation

• URL: http://arxiv.org/abs/2308.01966v1
• Date: Mon, 31 Jul 2023 06:02:35 GMT
• Title: DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation
• Authors: Vu Ngoc Tu, Van Thong Huynh, Hyung-Jeong Yang, M. Zaigham Zaheer, Shah Nawaz, Karthik Nandakumar, Soo-Hyung Kim
• Abstract summary: We introduce a convolutional Transformer for modeling and estimating human engagement. Our proposed system surpasses the baseline models, exhibiting a noteworthy $7$% improvement on test set. We employ different modality fusion mechanism and show that for this type of data, a simpled method with self-attention fusion gains the best performance.
• Score: 11.185293979235547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversational engagement estimation is posed as a regression problem, entailing the identification of the favorable attention and involvement of the participants in the conversation. This task arises as a crucial pursuit to gain insights into human's interaction dynamics and behavior patterns within a conversation. In this research, we introduce a dilated convolutional Transformer for modeling and estimating human engagement in the MULTIMEDIATE 2023 competition. Our proposed system surpasses the baseline models, exhibiting a noteworthy $7$\% improvement on test set and $4$\% on validation set. Moreover, we employ different modality fusion mechanism and show that for this type of data, a simple concatenated method with self-attention fusion gains the best performance.

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