Dialogue State Tracking
123 papers with code • 7 benchmarks • 11 datasets
Dialogue state tacking consists of determining at each turn of a dialogue the full representation of what the user wants at that point in the dialogue, which contains a goal constraint, a set of requested slots, and the user's dialogue act.
Libraries
Use these libraries to find Dialogue State Tracking models and implementationsDatasets
Latest papers
JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset
In this study, towards the advancement of research and development of task-oriented dialogue systems in Japanese, we constructed JMultiWOZ, the first Japanese language large-scale multi-domain task-oriented dialogue dataset.
Common Ground Tracking in Multimodal Dialogue
Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on ``dialogue state tracking'' (DST), which is the ability to update the representations of the speaker's needs at each turn in the dialogue by taking into account the past dialogue moves and history.
Encode Once and Decode in Parallel: Efficient Transformer Decoding
Transformer-based NLP models are powerful but have high computational costs that limit deployment scenarios.
State Value Generation with Prompt Learning and Self-Training for Low-Resource Dialogue State Tracking
Experimental results on the MultiWOZ 2. 1 dataset show that our method which has only less than 1 billion parameters achieves state-of-the-art performance under the data ratio settings of 5%, 10%, and 25% when limited to models under 100 billion parameters.
Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking
We address this by investigating synthetic audio data for audio-based DST.
Detecting agreement in multi-party dialogue: evaluating speaker diarisation versus a procedural baseline to enhance user engagement
Conversational agents participating in multi-party interactions face significant challenges in dialogue state tracking, since the identity of the speaker adds significant contextual meaning.
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users
While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively.
Towards LLM-driven Dialogue State Tracking
In this study, we conduct an initial examination of ChatGPT's capabilities in DST.
Turn-Level Active Learning for Dialogue State Tracking
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems.
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking
Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, but ignore unlabelled data in the target domain.