# Robust Zero-Shot Cross-Domain Slot Filling with Example Values

arXiv: https://arxiv.org/abs/1906.06870 · HTML: https://mlsanity.com/paper/1906.06870/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 30 of 38)

- [Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking](https://mlsanity.com/paper/2105.04222/) (2021)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [Description-Driven Task-Oriented Dialog Modeling](https://mlsanity.com/paper/2201.08904/) (2022)
- [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)
- [Learning to Select External Knowledge with Multi-Scale Negative Sampling](https://mlsanity.com/paper/2102.02096/) (2021)
- [Schema-Guided Paradigm for Zero-Shot Dialog](https://mlsanity.com/paper/2106.07056/) (2021)
- [Linguistically-Enriched and Context-Aware Zero-shot Slot Filling](https://mlsanity.com/paper/2101.06514/) (2021)
- [GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling](https://mlsanity.com/paper/2106.07055/) (2021)
- [Generative Conversational Networks](https://mlsanity.com/paper/2106.08484/) (2021)
- [Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling](https://mlsanity.com/paper/2104.07224/) (2021)
- [AnyTOD: A Programmable Task-Oriented Dialog System](https://mlsanity.com/paper/2212.09939/) (2022)
- [DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning](https://mlsanity.com/paper/2212.02851/) (2022)
- [Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting](https://mlsanity.com/paper/2307.02830/) (2023)
- [Few-shot Learning for Slot Tagging with Attentive Relational Network](https://mlsanity.com/paper/2103.02333/) (2021)
- [Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.07343/) (2021)
- [A Survey of Ontology Expansion for Conversational Understanding](https://mlsanity.com/paper/2410.15019/) (2024)
- [DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task](https://mlsanity.com/paper/2310.10169/) (2023)
- [MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging](https://mlsanity.com/paper/2108.11635/) (2021)
- [mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot Filling](https://mlsanity.com/paper/2203.12940/) (2022)
- [Semi-Supervised Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2109.08754/) (2021)
- [Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data](https://mlsanity.com/paper/2307.01764/) (2023)
- [Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey](https://mlsanity.com/paper/2307.09021/) (2023)
- [Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue System](https://mlsanity.com/paper/2105.14313/) (2021)
- [Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks](https://mlsanity.com/paper/2207.11363/) (2022)
- [Toward Open-domain Slot Filling via Self-supervised Co-training](https://mlsanity.com/paper/2303.13801/) (2023)
- [Adapting Text-based Dialogue State Tracker for Spoken Dialogues](https://mlsanity.com/paper/2308.15053/) (2023)
- [Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling](https://mlsanity.com/paper/2310.15294/) (2023)
- [A Joint and Domain-Adaptive Approach to Spoken Language Understanding](https://mlsanity.com/paper/2107.11768/) (2021)
- [GKS: Graph-based Knowledge Selector for Task-oriented Dialog System](https://mlsanity.com/paper/2112.03719/) (2021)

## Abstract (arXiv)

Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites. Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas. We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas. Our approach outperforms state-of-the-art models on two multi-domain datasets, especially in the low-data setting.