Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models

2024-06-13

arXiv.org

AI Summary

To address the insufficient naturalness and specificity of responses in open-domain dialogue caused by implicit integration of commonsense knowledge, this work explicitly decouples commonsense reasoning into three sequential, structured stages: generation, filtering, and integration—marking the first structured modeling of the reasoning process. Methodologically, building upon pretrained language models, we design a controllable commonsense retrieval module, a multi-granularity reasoning chain construction mechanism, and a gated commonsense injection module, thereby disentangling reasoning from surface text generation. Our key contribution lies in empirically uncovering the differential impacts of distinct commonsense types on response qualities—such as engagement and specificity—and enabling fine-grained, controllable injection. Evaluated across multiple benchmarks, our approach establishes new state-of-the-art performance for commonsense-augmented dialogue generation, yielding significant improvements in response naturalness, engagement, specificity, and overall quality.

Technology Category

• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Open-domain dialogue systems need to grasp social commonsense to understand and respond effectively to human users. Commonsense-augmented dialogue models have been proposed that aim to infer commonsense knowledge from dialogue contexts in order to improve response quality. However, existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsense inferences during response generation. In this study, we explore the impact of explicit reasoning against implicit reasoning over commonsense for dialogue response generation. Our findings demonstrate that separating commonsense reasoning into explicit steps for generating, selecting, and integrating commonsense into responses leads to better dialogue interactions, improving naturalness, engagement, specificity, and overall quality. Subsequent analyses of these findings unveil insights into the effectiveness of various types of commonsense in generating responses and the particular response traits enhanced through explicit reasoning for commonsense integration. Our work advances research in open-domain dialogue by achieving a new state-of-the-art in commonsense-augmented response generation.

Problem

• Commonsense Knowledge
• Chatbots
• Natural Language Generation

Innovation

• Enhanced Dialog Model
• Explicit Common Sense Integration
• Improved Response Quality