# Transforming Slot Schema Induction with Generative Dialogue State Inference

arXiv: https://arxiv.org/abs/2408.01638 · HTML: https://mlsanity.com/paper/2408.01638/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 3 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- [Unsupervised Slot Schema Induction for Task-oriented Dialog](https://mlsanity.com/paper/2205.04515/) (2022)
- [Structure Extraction in Task-Oriented Dialogues with Slot Clustering](https://mlsanity.com/paper/2203.00073/) (2022)

## Built on by (most cited first, 3 of 3)

- [Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactions](https://mlsanity.com/paper/2504.18474/) (2025)
- [Text-to-SQL Task-oriented Dialogue Ontology Construction](https://mlsanity.com/paper/2507.23358/) (2025)
- [AutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory](https://mlsanity.com/paper/2609.21940/) (2026)

## Abstract (arXiv)

The challenge of defining a slot schema to represent the state of a task-oriented dialogue system is addressed by Slot Schema Induction (SSI), which aims to automatically induce slots from unlabeled dialogue data. Whereas previous approaches induce slots by clustering value spans extracted directly from the dialogue text, we demonstrate the power of discovering slots using a generative approach. By training a model to generate slot names and values that summarize key dialogue information with no prior task knowledge, our SSI method discovers high-quality candidate information for representing dialogue state. These discovered slot-value candidates can be easily clustered into unified slot schemas that align well with human-authored schemas. Experimental comparisons on the MultiWOZ and SGD datasets demonstrate that Generative Dialogue State Inference (GenDSI) outperforms the previous state-of-the-art on multiple aspects of the SSI task.