# Schema-Guided Paradigm for Zero-Shot Dialog

arXiv: https://arxiv.org/abs/2106.07056 · HTML: https://mlsanity.com/paper/2106.07056/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 13 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue](https://mlsanity.com/paper/2009.13570/) (2020)
- [STAR: A Schema-Guided Dialog Dataset for Transfer Learning](https://mlsanity.com/paper/2010.11853/) (2020)
- [Domain Adaptive Dialog Generation via Meta Learning](https://mlsanity.com/paper/1906.03520/) (2019)
- [Towards Zero-Shot Frame Semantic Parsing for Domain Scaling](https://mlsanity.com/paper/1707.02363/) (2017)
- [Robust Zero-Shot Cross-Domain Slot Filling with Example Values](https://mlsanity.com/paper/1906.06870/) (2019)
- [Schema-Guided Dialogue State Tracking Task at DSTC8](https://mlsanity.com/paper/2002.01359/) (2020)
- [GSN: A Graph-Structured Network for Multi-Party Dialogues](https://mlsanity.com/paper/1905.13637/) (2019)
- [Zero-Shot Dialog Generation with Cross-Domain Latent Actions](https://mlsanity.com/paper/1805.04803/) (2018)
- [Structured Attention for Unsupervised Dialogue Structure Induction](https://mlsanity.com/paper/2009.08552/) (2020)
- [Unsupervised Dialog Structure Learning](https://mlsanity.com/paper/1904.03736/) (2019)
- [Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability](https://mlsanity.com/paper/1706.08476/) (2017)
- [End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning](https://mlsanity.com/paper/1606.01269/) (2016)
- [Discovering Dialog Structure Graph for Open-Domain Dialog Generation](https://mlsanity.com/paper/2012.15543/) (2020)

## Built on by (most cited first, 14 of 14)

- [Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning](https://mlsanity.com/paper/2305.13660/) (2023)
- [SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting](https://mlsanity.com/paper/2305.09067/) (2023)
- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- [LAD: Language Models as Data for Zero-Shot Dialog](https://mlsanity.com/paper/2207.14393/) (2022)
- [AnyTOD: A Programmable Task-Oriented Dialog System](https://mlsanity.com/paper/2212.09939/) (2022)
- [Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents](https://mlsanity.com/paper/2502.17321/) (2025)
- [Dialog Flow Induction for Constrainable LLM-Based Chatbots](https://mlsanity.com/paper/2408.01623/) (2024)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey](https://mlsanity.com/paper/2307.09021/) (2023)
- [A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient](https://mlsanity.com/paper/2207.07276/) (2022)
- [A Neuro-Symbolic Approach to Monitoring Salt Content in Food](https://mlsanity.com/paper/2404.01182/) (2024)
- [CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment](https://mlsanity.com/paper/2506.02264/) (2025)
- [Graph Integrated Language Transformers for Next Action Prediction in Complex Phone Calls](https://mlsanity.com/paper/2404.08155/) (2024)
- [Following the Flow the Law Requires: Training Voice Agents to Transfer to Unseen Regulated Procedures in Debt Collection](https://mlsanity.com/paper/2610.18881/) (2026)

## Abstract (arXiv)

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.