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 …

The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Recent large language models (LLMs) have advanced the quality of open-ended
conversations with chatbots. Although LLM-driven chatbots have the potential to support …

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however
there are few convincing demonstrations of this to date. The most popular sequence to …

Large pre-trained language models have been shown to store factual knowledge in their
parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks …

Building socialbots that can have deep, engaging open-domain conversations with humans
is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able …

Messages in human conversations inherently convey emotions. The task of detecting
emotions in textual conversations leads to a wide range of applications such as opinion …

There is a resurgent interest in developing intelligent open-domain dialog systems due to
the availability of large amounts of conversational data and the recent progress on neural …

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances
by either ignoring or passively agreeing with them. To address this issue, we introduce …

A significant barrier to progress in data-driven approaches to building dialog systems is the
lack of high quality, goal-oriented conversational data. To help satisfy this elementary …