Numerous online conversations are produced on a daily basis, resulting in a pressing need to conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts, such as 79 vs. 73 MRR on Chinese customer service dialogues. We further probe into our outputs and shed light on how topics and discourse indicate conversational user interactions.

翻译：许多在线对话都是每天制作的,因此迫切需要对话理解。作为构建讨论的基础,我们确定对话对话中的反应关系,将回应的言论与其启动过程联系起来。为了了解谁回应了谁,我们在这里探讨主题内容的一致性和话语作用的依赖性如何表明这种互动,而大多数先前的工作忽视了文字发生背后的潜在因素的影响。我们提出了一个模式,以在文字传播中学习潜在主题和话语,并通过利用主题一致性和对话依赖性来预测双向的启动-反应联系。英语和中文对话的实验结果表明,我们的模型大大优于以往的艺术状态,例如,中国客户服务对话中的79对73 MRR。我们进一步探讨我们的产出,并阐明专题和话语如何显示对话用户互动。