Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

While communicating with a user, a task-oriented dialogue system has to track the user's
needs at each turn according to the conversation history. This process called dialogue state …

We demonstrate substantial performance gains in zero-shot dialogue state tracking (DST) by
enhancing training data diversity through synthetic data generation. Existing DST datasets …

A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains
without using any supervised data, zero-shot domain adaptation. Parameter-Efficient …

Few-shot anomaly detection (FSAD) is essential in industrial manufacturing. However,
existing FSAD methods struggle to effectively leverage a limited number of normal samples …

This work demonstrates that substantial gains in zero-shot dialogue state tracking (DST)
accuracy can be achieved by increasing the diversity of training data using synthetic data …

Creating effective and reliable task-oriented dialog systems (ToDSs) is challenging, not only
because of the complex structure of these systems, but also due to the scarcity of training …

Dialogue state tracking (DST) provides necessary information for the policy learning module
to decide the next action according to the historical state and the utterance related details of …

This work investigates improving domain adaptability in Dialogue State Tracking (DST), a
crucial task for integrating conversational AI to real-world software applications. DST …

Training a dialogue generation model requires large-scale, high-quality dialogue data. Data
augmentation is a common method to expand a dataset. Some common data augmentation …