# Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems

arXiv: https://arxiv.org/abs/2001.07526 · HTML: https://mlsanity.com/paper/2001.07526/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation](https://mlsanity.com/paper/2306.04724/) (2023)
- [Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue](https://mlsanity.com/paper/2110.06905/) (2021)
- [SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services](https://mlsanity.com/paper/2105.08049/) (2021)
- [Actively Discovering New Slots for Task-oriented Conversation](https://mlsanity.com/paper/2305.04049/) (2023)
- [Predictability and Causality in Spanish and English Natural Language Generation](https://mlsanity.com/paper/2408.14283/) (2024)

## Abstract (arXiv)

In task-oriented dialogue systems the dialogue state tracker (DST) component is responsible for predicting the state of the dialogue based on the dialogue history. Current DST approaches rely on a predefined domain ontology, a fact that limits their effective usage for large scale conversational agents, where the DST constantly needs to be interfaced with ever-increasing services and APIs. Focused towards overcoming this drawback, we propose a domain-aware dialogue state tracker, that is completely data-driven and it is modeled to predict for dynamic service schemas. The proposed model utilizes domain and slot information to extract both domain and slot specific representations for a given dialogue, and then uses such representations to predict the values of the corresponding slot. Integrating this mechanism with a pretrained language model (i.e. BERT), our approach can effectively learn semantic relations.