Conversation Kernels: A Flexible Mechanism to Learn Relevant Context for Online Conversation Understanding

2025-05-26

AI Summary

Online dialogue understanding faces challenges including strong implicit coreference in short texts and complex contextual dependencies. To address these, we propose Conversation Kernels—a novel mechanism that formally characterizes dialogue tree neighborhood exploration as two composable kernel functions, enabling task-adaptive dynamic construction of local substructures. Our method jointly integrates dialogue tree neighborhood traversal, task-conditioned kernel weight learning, and end-to-end encoding via pretrained language models, supporting fine-grained, multi-objective (e.g., interestingness, depth) and multi-label dialogue attribute identification. Evaluated on the Slashdot dataset, our approach achieves significant improvements across all metrics, demonstrating strong cross-task transferability, generalizability, and flexibility—particularly for heterogeneous dialogue understanding tasks.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Kernel Methods

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: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Understanding online conversations has attracted research attention with the growth of social networks and online discussion forums. Content analysis of posts and replies in online conversations is difficult because each individual utterance is usually short and may implicitly refer to other posts within the same conversation. Thus, understanding individual posts requires capturing the conversational context and dependencies between different parts of a conversation tree and then encoding the context dependencies between posts and comments/replies into the language model. To this end, we propose a general-purpose mechanism to discover appropriate conversational context for various aspects about an online post in a conversation, such as whether it is informative, insightful, interesting or funny. Specifically, we design two families of Conversation Kernels, which explore different parts of the neighborhood of a post in the tree representing the conversation and through this, build relevant conversational context that is appropriate for each task being considered. We apply our developed method to conversations crawled from slashdot.org, which allows users to apply highly different labels to posts, such as 'insightful', 'funny', etc., and therefore provides an ideal experimental platform to study whether a framework such as Conversation Kernels is general-purpose and flexible enough to be adapted to disparately different conversation understanding tasks.

Problem

• Understanding short, context-dependent online conversation posts
• Capturing conversational context and dependencies in language models
• Developing flexible mechanisms for diverse conversation understanding tasks

Innovation

• Conversation Kernels capture context dependencies
• Explore neighborhood of posts in conversation trees
• General-purpose mechanism for diverse conversation tasks