Dynamic Commonsense Coordination for Empathetic Response Generation

2026-07-27 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

Zhengyu Qi proposes DCC, a Dynamic Commonsense Coordination Framework, to enhance Empathetic Response Generation (ERG) by addressing limitations of static commonsense utilization. DCC integrates three modules: residual-based commonsense interaction (SCE-AttnRes) for contextual and situational commonsense, Association-Guided Commonsense Filtering (AGCF) to down-weight low-relevance relations, and Iterative Commonsense-Aware Decoding (ICAD) for dynamic retrieval during generation. Experiments on the Empathetic-Dialogues benchmark show DCC improves emotion classification accuracy and response diversity over the CEM baseline, while maintaining comparable perplexity. An LLM-based blind evaluation using DeepSeek-V3.2 in July 2026 further demonstrates DCC generates responses with better relevance, coherence, and informativeness. DCC increases parameters by 10.32% (from 17.49M to 19.30M) and latency by 32.55% (from 83.81ms to 111.10ms per response), achieving 9.00 samples per second on an NVIDIA GeForce RTX 4090 GPU.

Key takeaway

For machine learning engineers developing empathetic dialogue systems, consider implementing dynamic commonsense coordination to improve response quality. While DCC increases latency by 32.55% and parameters by 10.32%, its gains in relevance, coherence, and informativeness, validated by LLM-based evaluation, can justify the computational trade-off. Evaluate your system's specific needs for contextual specificity versus raw throughput.

Key insights

Dynamic coordination of commonsense across dialogue stages significantly improves empathetic response quality.

Principles

• Commonsense utilization should adapt dynamically across understanding and generation.
• Source-specific processing of commonsense memories yields distinct decoding behaviors.
• Filtering low-relevance commonsense relations enhances response quality.

Method

DCC employs SCE-AttnRes for cross-source integration, AGCF for relation-level filtering, and ICAD for token-level commonsense retrieval during response generation, enabling stage-aware coordination.

In practice

• Implement residual-based attention for integrating diverse commonsense sources.
• Use association-guided filtering to prune irrelevant knowledge.
• Apply iterative, token-dependent commonsense retrieval during decoding.

Topics

• Empathetic Response Generation
• Commonsense Knowledge
• Dialogue Systems
• Natural Language Generation
• DeepSeek-V3.2
• COMET

Code references

• Hanabi-Q/DCC-ERG

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.