Controlling Multimodal Conversational Agents with Coverage-Enhanced Latent Actions (2026.acl-long)
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| Challenge: | Recent reinforcement learning (RL) has been widely explored for adapting MCAs to various human-AI interaction scenarios. |
| Approach: | They propose to use a latent action space for reinforcement learning instead of RL to fine-tune MCAs. |
| Outcome: | The proposed method outperforms baselines on two conversation tasks with a novel cycle consistency loss. |
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| Challenge: | Existing approaches to define action spaces for conversational agents have limitations . end-to-end dialog systems can handle complex domains with limited action space . |
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Nurul Lubis, Christian Geishauser, Michael Heck, Hsien-chin Lin, Marco Moresi, Carel van Niekerk, Milica Gasic
| Challenge: | Reinforcement learning (RL) can be used to steer a conversation towards successful task completion. |
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| Challenge: | Existing approaches to offline reinforcement learning (RL) focus on learning value functions or policy gradients, but they view it as a sequence modeling task. |
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| Challenge: | Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size. |
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| Challenge: | Existing methods for dialog agent training lack a robust action space for entangled information, which can cause bias and deviate from natural human language. |
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| Challenge: | Conversational assistants are increasingly popular across diverse real-world applications . speech data constitute high-dimensional signals that are difficult to model even for frontier models . |
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| Challenge: | State-of-the-art large multimodal models face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task. |
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A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition (2025.coling-main)
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| Challenge: | Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contributions during fusion. |
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PropGenie: A Multi-Agent Conversational Framework for Real Estate Assistance (2026.eacl-demo)
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| Challenge: | PropGenie is a multi-agent framework based on large language models (LLMs) it provides comprehensive real estate assistance in real-world scenarios . |
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