Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …

Recent research attention for relation extraction has been paid to the dialogue scenario, ie,
dialoguelevel relation extraction (DiaRE). Existing DiaRE methods either simply …

Human–computer conversation has long been an interest of artificial intelligence and
natural language processing research. Recent years have seen a dramatic improvement in …

We study the learning of a matching model for dialogue response selection. Motivated by the
recent finding that models trained with random negative samples are not ideal in real-world …

As an important branch of current dialogue systems, retrieval-based chatbots leverage
information retrieval to select proper predefined responses. Various promising architectures …

Response selection plays a vital role in building retrieval-based conversation systems.
Despite that response selection is naturally a learning-to-rank problem, most prior works …

Inspired by studies on the overwhelming presence of experience-sharing in human-human
conversations, Emora, the social chatbot developed by Emory University, aims to bring such …

We investigate response selection for multi-turn conversation in retrieval-based chatbots.
Existing studies pay more attention to the matching between utterances and responses by …

Multi-Choice Reading Comprehension~(MCRC) is an essential task where a machine
selects the correct answer from multiple choices given a context document and a …