Advancements in conversational systems have revolutionized information access,
surpassing the limitations of single queries. However, developing dialogue systems requires …

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

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 …

The task-oriented dialogue domain system requires classifying intent and replying to a
specific goal domain. In the sub-module of Task-oriented, the Dialogue State Tracker (DST) …

Abstract The goal of Dialogue State Tracking (DST) is to track the user's intents and goals in
each conversation round based on a given dialogue history. Existing DST approaches …

Recognizing dialog acts of users is an essential component in building successful
conversational agents. In this work, we propose a dialog act (DA) classifier for two of our …

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

When designing robots to assist in everyday human activities, it is crucial to enhance user
requests with visual cues from their surroundings for improved intent understanding. This …