# Zero-Shot Adaptive Transfer for Conversational Language Understanding

arXiv: https://arxiv.org/abs/1808.10059 · HTML: https://mlsanity.com/paper/1808.10059/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2018.

## Built on by (most cited first, 20 of 20)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling](https://mlsanity.com/paper/2110.03572/) (2021)
- [Linguistically-Enriched and Context-Aware Zero-shot Slot Filling](https://mlsanity.com/paper/2101.06514/) (2021)
- [Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling](https://mlsanity.com/paper/2104.07224/) (2021)
- [A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking](https://mlsanity.com/paper/2201.05780/) (2022)
- [Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning](https://mlsanity.com/paper/2301.10915/) (2023)
- [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)
- [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)
- [Toward Open-domain Slot Filling via Self-supervised Co-training](https://mlsanity.com/paper/2303.13801/) (2023)
- [Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling](https://mlsanity.com/paper/2310.15294/) (2023)
- [Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning](https://mlsanity.com/paper/2110.08254/) (2021)
- [Zero-Shot Learning for Joint Intent and Slot Labeling](https://mlsanity.com/paper/2212.07922/) (2022)
- [Low-Resource Compositional Semantic Parsing with Concept Pretraining](https://mlsanity.com/paper/2301.09809/) (2023)
- [HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling](https://mlsanity.com/paper/2310.09135/) (2023)
- [An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistants](https://mlsanity.com/paper/2406.08848/) (2024)
- [Actively Discovering New Slots for Task-oriented Conversation](https://mlsanity.com/paper/2305.04049/) (2023)
- [Match Chat: Real Time Generative AI and Generative Computing for Tennis](https://mlsanity.com/paper/2509.12592/) (2025)

## Abstract (arXiv)

Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled data is required for training each new domain. While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments. To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments. Extensive experimentation over a dataset of 10 domains relevant to our commercial personal digital assistant shows that our model outperforms previous state-of-the-art systems by a large margin, and achieves an even higher improvement in the low data regime.