Multi-turn dialogue reading comprehension aims to teach machines to read dialogue
contexts and solve tasks such as response selection and answering questions. The major …

Answer selection in open-domain dialogues aims to select an accurate answer from
candidates. The recent success of answer selection models hinges on training with large …

We study the problem of coarse-grained response selection in retrieval-based dialogue
systems. The problem is equally important with fine-grained response selection, but is less …

Recently, research on open domain dialogue systems have attracted extensive interests of
academic and industrial researchers. The goal of an open domain dialogue system is to …

Information-seeking dialogue systems are widely used in e-commerce systems, with
answers that must be tailored to fit the specific settings of the online system. Given the user …

Coarse-grained response selection is a fundamental and essential subsystem for the widely
used retrieval-based chatbots, aiming to recall a coarse-grained candidate set from a large …

Recent progress in deep learning has continuously improved the accuracy of dialogue
response selection. However, in real-world scenarios, the high computation cost forces …

Dialogue systems powered by conversational artificial intelligence (AI) have never been so
popular. Interacting with computer through languages reveals a more natural interface to …

Task-oriented dialogue systems have become overwhelmingly popular in recent
researches. Spoken Language Understanding (SLU) is widely used to extract the semantics …

Proper response selection is a crucial challenge in retrieval-based chatbots. The state-of-the-
art methods match a response with the word sequence of a context, or match the response …