Keep and Select: Improving Hierarchical Context Modeling for Multi-Turn Response Generation.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Hierarchical context modeling is crucial for multi-turn conversational systems.
• Existing methods often treat context utterances independently, neglecting adjacent relationships and the query's significance.
• Attention mechanisms are commonly used but may not fully capture nuanced contextual dependencies.

Purpose of the Study:

• To propose a novel multi-turn response generation model, KS-CQ.
• To enhance context representation by incorporating neighbor-aware semantics.
• To improve query representation by selectively enriching it with contextual information.

Main Methods:

• Developed the KS-CQ model with Keep and Select modules.
• The Keep module recodes context utterances using neighboring utterance semantics.
• The Select module enriches query representation with background context.

Main Results:

• KS-CQ demonstrated superior performance on two benchmark datasets.
• Both automatic and human evaluations confirmed the model's effectiveness.
• The neighbor-aware context and context-enriched query representations proved beneficial.

Conclusions:

• The proposed KS-CQ model effectively models hierarchical context in multi-turn conversations.
• Considering utterance relationships and query importance enhances response generation.
• KS-CQ offers a significant advancement over existing state-of-the-art methods.