# Towards Zero-Shot Frame Semantic Parsing for Domain Scaling

arXiv: https://arxiv.org/abs/1707.02363 · HTML: https://mlsanity.com/paper/1707.02363/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2017.

## Built on by (most cited first, 30 of 40)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking](https://mlsanity.com/paper/2105.04222/) (2021)
- [Neural Data Augmentation via Example Extrapolation](https://mlsanity.com/paper/2102.01335/) (2021)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex](https://mlsanity.com/paper/2301.12868/) (2023)
- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- [Multimodal Conversational AI: A Survey of Datasets and Approaches](https://mlsanity.com/paper/2205.06907/) (2022)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Schema-Guided Paradigm for Zero-Shot Dialog](https://mlsanity.com/paper/2106.07056/) (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)
- [GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling](https://mlsanity.com/paper/2106.07055/) (2021)
- [Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling](https://mlsanity.com/paper/2104.07224/) (2021)
- [Style Control for Schema-Guided Natural Language Generation](https://mlsanity.com/paper/2109.12211/) (2021)
- [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)
- [Good-Enough Example Extrapolation](https://mlsanity.com/paper/2109.05602/) (2021)
- [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)
- [Spoken Conversational Agents with Large Language Models](https://mlsanity.com/paper/2512.02593/) (2025)
- [Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing](https://mlsanity.com/paper/2206.05352/) (2022)
- [Capture Salient Historical Information: A Fast and Accurate Non-Autoregressive Model for Multi-turn Spoken Language Understanding](https://mlsanity.com/paper/2206.12209/) (2022)
- [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)
- [Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels](https://mlsanity.com/paper/2305.03793/) (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)
- [Reprint: a randomized extrapolation based on principal components for data augmentation](https://mlsanity.com/paper/2204.12024/) (2022)

## Abstract (arXiv)

State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods. While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems. This paper proposes a deep learning based approach that can utilize only the slot description in context without the need for any labeled or unlabeled in-domain examples, to quickly bootstrap a new domain. The main idea of this paper is to leverage the encoding of the slot names and descriptions within a multi-task deep learned slot filling model, to implicitly align slots across domains. The proposed approach is promising for solving the domain scaling problem and eliminating the need for any manually annotated data or explicit schema alignment. Furthermore, our experiments on multiple domains show that this approach results in significantly better slot-filling performance when compared to using only in-domain data, especially in the low data regime.