Recent years have witnessed a surge of interest in the field of open-domain dialogue.
Thanks to the rapid development of social media, large dialogue corpus from the Internet …

Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero
and few shot learning could greatly benefit dialogue state tracking (DST). In this work, we …

Responding with multi-modal content has been recognized as an essential capability for an
intelligent conversational agent. In this paper, we introduce the MMDialog dataset to better …

In this work, we tackle the scenario of understanding characters in scripts, which aims to
learn the characters' personalities and identities from their utterances. We begin by …

While textual information significantly enhances the performance of pre-trained language
models (PLMs) in knowledge graph completion (KGC), the static and noisy nature of existing …

Pre-trained language models (PrLMs) have demonstrated superior performance due to their
strong ability to learn universal language representations from self-supervised pre-training …

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 …

Recent data-driven conversational models are able to return fluent, consistent, and
informative responses to many kinds of requests and utterances in task-oriented scenarios …

In this paper, we investigate the task of response ranking in conversational legal search. We
propose a novel method for conversational passage response retrieval (ConvPR) for long …

As knowledge-grounded dialogue systems are attracting intense research interest,
technology that facilitates reference to various types of external knowledge as dialogue …