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

TL;DR

This paper introduces Conversation Kernels, a flexible mechanism to capture relevant conversational context in online discussions, improving understanding of posts' attributes across various tasks.

Contribution

It proposes two families of Conversation Kernels that explore conversation neighborhoods to encode context, demonstrating adaptability to different conversation understanding tasks.

Findings

Effective in capturing context for diverse conversation attributes

Applicable to various online discussion platforms

Enhances post attribute classification accuracy

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.