More to diverse: Generating diversified responses in a task oriented multimodal dialog system.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Computer Vision

Background:

• Multimodal dialogue systems are gaining research attention due to their wide applications.
• Generating diverse and informative responses is crucial for goal-oriented conversational agents.
• Existing systems often struggle to effectively integrate textual and visual information for response generation.

Purpose of the Study:

• To propose an end-to-end neural framework for generating diverse responses in multimodal dialogue systems.
• To capture and effectively fuse information from both text and image modalities.
• To enhance user experience through informative, diverse, and polite system responses.

Main Methods:

• Utilized a multimodal encoder with co-attention between text and image for contextual information.
• Employed the BLOCK fusion technique for effective information sharing across modalities.
• Implemented stochastic beam search with Gumble Top K-tricks for diversified response generation.

Main Results:

• The proposed framework significantly outperforms existing and baseline methods in generating diverse responses.
• The system successfully generates responses that are informative, interesting, and polite without information loss.
• Empirical evaluation confirms that integrating images improves the model's efficiency in generating diversified responses.

Conclusions:

• The developed multimodal dialogue system framework effectively generates diverse, informative, and polite responses.
• The BLOCK fusion technique and stochastic beam search are key to achieving improved response diversity.
• Integrating visual information alongside text enhances the capabilities of conversational AI systems.