# Hybrid Dialogue State Tracking for Persian Chatbots: A Language Model-Based Approach

arXiv: https://arxiv.org/abs/2510.01052 · HTML: https://mlsanity.com/paper/2510.01052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [In-Context Learning for Few-Shot Dialogue State Tracking](https://mlsanity.com/paper/2203.08568/) (2022)
- [Towards LLM-driven Dialogue State Tracking](https://mlsanity.com/paper/2310.14970/) (2023)
- [Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation](https://mlsanity.com/paper/2405.13037/) (2024)
- [Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances](https://mlsanity.com/paper/2204.03375/) (2022)
- [PerSHOP -- A Persian dataset for shopping dialogue systems modeling](https://mlsanity.com/paper/2401.00811/) (2024)

## Abstract (arXiv)

Dialogue State Tracking (DST) is an essential element of conversational AI with the objective of deeply understanding the conversation context and leading it toward answering user requests. Due to high demands for open-domain and multi-turn chatbots, the traditional rule-based DST is not efficient enough, since it cannot provide the required adaptability and coherence for human-like experiences in complex conversations. This study proposes a hybrid DST model that utilizes rule-based methods along with language models, including BERT for slot filling and intent detection, XGBoost for intent validation, GPT for DST, and online agents for real-time answer generation. This model is uniquely designed to be evaluated on a comprehensive Persian multi-turn dialogue dataset and demonstrated significantly improved accuracy and coherence over existing methods in Persian-based chatbots. The results demonstrate how effectively a hybrid approach may improve DST capabilities, paving the way for conversational AI systems that are more customized, adaptable, and human-like.