基于神经相干模型的会话数据线索重构
Thread Reconstruction in Conversational Data using Neural Coherence Models
摘要：
论坛是重要的信息来源。它们通常用于回答用户可能遇到的特定问题，并发现有关感兴趣主题的更多信息。这些论坛中的讨论可能会以复杂的方式发展，使用户难以跟上思想的流动。我们提出了一种新的方法，用于自动识别论坛讨论的底层线程结构。我们的方法是基于一个神经模型，该模型计算可能重建的一致性得分，然后选择得分最高的，即，最连贯的一个。初步实验表明，有前途的结果优于一些强大的基线方法。
英文摘要：
Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
