Papers by Duc-Trong Le
Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition (2024.lrec-main)
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| Challenge: | Existing models for multimodal Emotion Recognition in conversation (ERC) use text as the main modality for emotion recognition. |
| Approach: | They propose a Directed Acyclic Graph (DAG) approach that integrates textual, acoustic, and visual features within a unified framework. |
| Outcome: | The proposed model outperforms baseline models on the IEMOCAP and MELD datasets. |
Multimodal Review Generation with Privacy and Fairness Awareness (2020.coling-main)
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| Challenge: | Existing frameworks for generating personalized reviews take privacy and fairness into account . users generate digital footprints when "traveling" on the internet . |
| Approach: | They propose a neural-based framework that generates personalized reviews with privacy and fairness in mind. |
| Outcome: | The proposed framework generates plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased embeddings. |
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction (2023.emnlp-main)
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| Challenge: | Emotion recognition is a crucial task for human conversation understanding . multimodal data, e.g., language, voice, and facial expressions, add complexity to the task. |
| Approach: | They propose a relational temporal Graph Neural Network with Auxiliary Cross-Modality Interaction framework that captures conversation-level cross-modality interactions and utterance-level temporal dependencies with modality-specific manner for conversation understanding. |
| Outcome: | The proposed framework captures conversation-level cross-modality interactions and utterance-level temporal dependencies with the modality-specific manner for conversation understanding. |