Abstract Topic modeling using models such as Latent Dirichlet Allocation (LDA) is a text mining technique to extract human-readable semantic “topics” (i.e., word clusters) from corpus of textual documents. In software engineering, topic has been used analyze data in empirical studies (e.g., find out what developers talk about online), but also build new techniques support engineering tasks sour...

In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select most appropriate response according extracting salient features in context utterances. As conversation goes on, topic shift at discourse-level naturally happens through continuous context. However, all known systems are satisfied with exploiting local words for utterance representation but fail capture such es...

The abundant sequential documents such as online archival, social media, and news feeds are streamingly updated, where each chunk of is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden topics their temporal dependencies. However, most the existing approaches focus single-topic-thread evolutio...