As the labeling cost for different modules in task-oriented dialog (ToD) systems is high, a
major challenge is to learn different tasks with the least amount of labeled data. Recently …

With the rapid progress of deep neural models and the explosion of available data
resources, dialogue systems that supports extensive topics and chit-chat conversations are …

Due to the widespread use of text in cyberspace, linguistic steganography, which hides
secret information into normal texts, develops quickly in these years. While linguistic …

Pre-trained code models have recently achieved substantial improvements in many code
intelligence tasks. These models are first pre-trained on large-scale unlabeled datasets in a …

This review gives an extensive overview of evaluation methods for task-oriented dialogue
systems, paying special attention to practical applications of dialogue systems, for example …

Personality recognition plays a pivotal role when developing user-centric solutions such as
recommender systems or decision support systems across various domains, including …

Task-oriented dialogue systems (TOD) are blossoming with the advances in pre-trained
language models (PrLM). Recently, research on PrLM-based multi-domain TOD has arisen …

As the labeling costs in legal artificial intelligence tasks are expensive. Therefore, it
becomes a challenge to utilize low cost to train a robust model. In this paper, we propose a …

In task-oriented dialogues, dialogue state tracking (DST) is a critical component as it
identifies specific information for the user's purpose. However, as annotating DST data …

Neural language models (NLMs) are susceptible to producing inconsistent output. This
paper proposes a new diagnosis as well as a novel remedy for NLMs' incoherence. We train …