End-to-end dialogue structure parsing on multi-floor dialogue based on multi-task learning.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Human-Robot Interaction

Background:

• Multi-floor dialogues involve multiple participants conversing across distinct conversational spaces.
• A key challenge is the complexity of inter-floor relationships and coordination by multi-communicating members to achieve shared goals.
• Existing methods struggle to accurately parse the intricate structures within and across these dialogue floors.

Purpose of the Study:

• To propose a novel neural dialogue structure parser designed for multi-floor dialogues.
• To automatically identify and analyze the complex dialogue structures in collaborative robot navigation.
• To enhance the consistency and accuracy of multi-floor dialogue structure parsing.

Main Methods:

• Developed a neural dialogue structure parser incorporating an attention mechanism.
• Applied multi-task learning to simultaneously learn dialogue structure and response prediction.
• Utilized dialogue response prediction as an auxiliary task to improve structure parsing consistency.

Main Results:

• The proposed model demonstrated improved performance in parsing multi-floor dialogue structures compared to conventional approaches.
• The attention mechanism effectively captured complex relationships within and across dialogue floors.
• Multi-task learning and auxiliary prediction tasks enhanced parsing accuracy and consistency.

Conclusions:

• The developed neural parser effectively handles the complexity of multi-floor dialogues in human-robot interaction.
• The proposed method offers a significant advancement in automatically understanding and structuring collaborative conversations.
• This work provides a foundation for more sophisticated dialogue management in multi-agent systems.