提出对话核机制，精准捕捉在线对话上下文以理解帖子含义。
Conversation Kernels: A Flexible Mechanism to Learn Relevant Context for Online Conversation Understanding
- 设计两类对话核，从对话树中挖掘不同邻域信息构建上下文。
- 在Slashdot数据集上实现多标签任务的统一建模，效果优于基线。
- 适用于不同理解任务，如判断观点是否深刻或有趣，灵活性强。
随着社交网络和在线讨论论坛的发展，在线对话理解成为研究热点。由于单个发言通常简短且可能隐含引用对话树中的其他内容，理解发言需捕捉上下文依赖关系，并将这些依赖编码进语言模型。为此，我们提出一种通用机制，可为不同类型的问题（如判断发言是否具有信息量、洞察力、趣味性）发现合适的对话上下文。具体地，设计了两组对话核，通过探索发言在对话树中的不同邻域，构建针对特定任务的上下文。我们在从slashdot.org爬取的对话数据上验证该方法，该数据允许用户对发言打上多种标签（如'insightful', 'funny'等），提供了理想的实验平台。结果表明，对话核框架具备通用性和灵活性，能有效适应差异较大的对话理解任务。
原文摘要 · Abstract (English)
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.
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