It is often challenging to teach specialized, unseen tasks to dialogue systems due to the high cost of expert knowledge, training data, and high technical difficulty. To support domain-specific applications - such as law, medicine, or finance - it is essential to build frameworks that enable non-technical experts to define, test, and refine system behaviour with minimal effort. Achieving this requires cross-disciplinary collaboration between developers and domain specialists. In this work, we introduce a novel framework, CoDial (Code for Dialogue), that converts expert knowledge, represented as a novel structured heterogeneous graph, into executable conversation logic. CoDial can be easily implemented in existing guardrailing languages, such as Colang, to enable interpretable, modifiable, and true zero-shot specification of task-oriented dialogue systems. Empirically, CoDial achieves state-of-the-art performance on the STAR dataset for inference-based models and is competitive with similar baselines on the well-known MultiWOZ dataset. We also demonstrate CoDial's iterative improvement via manual and LLM-aided feedback, making it a practical tool for expert-guided alignment of LLMs in high-stakes domains.
View on arXiv@article{shayanfar2025_2506.02264,
title={ CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment },
author={ Radin Shayanfar and Chu Fei Luo and Rohan Bhambhoria and Samuel Dahan and Xiaodan Zhu },
journal={arXiv preprint arXiv:2506.02264},
year={ 2025 }
}