One of the core components of modern spoken dialogue systems is the belief tracker, which
estimates the user's goal at every step of the dialogue. However, most current approaches …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which
consist of slot-value pairs. As condensed structural information memorizing all history …

The dependencies between system and user utterances in the same turn and across
different turns are not fully considered in existing multidomain dialogue state tracking …

Few-shot dialogue state tracking (DST) is a realistic problem that trains the DST model with
limited labeled data. Existing few-shot methods mainly transfer knowledge learned from …

This paper discusses models for dialogue state tracking using recurrent neural networks
(RNN). We present experiments on the standard dialogue state tracking (DST) dataset …

Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing
DST models either ignore temporal feature dependencies across dialogue turns or fail to …

Dialog state tracking (DST) is the key component of goal-driven Spoken Dialog Systems.
Almost all existing dialog state trackers are unable to handle unknown slot values. The …

Spoken dialogue systems provide a natural conversational interface to computer
applications. In recent years, the substantial improvements in the performance of speech …

Dialogue systems are attracting more and more attention recently. Dialogue systems can be
categorized into open-domain dialogue systems and task-oriented dialogue systems. Task …