  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.
