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

## TL;DR

This paper introduces a robust zero-shot cross-domain slot filling method that leverages slot descriptions and example values, improving transferability and performance in low-data scenarios for dialog systems.

## Contribution

It proposes a novel approach combining slot descriptions with example values to enhance zero-shot slot filling robustness across misaligned schemas.

## Key findings

- Outperforms state-of-the-art models on multi-domain datasets.
- Effective in low-data and schema-misalignment scenarios.
- Demonstrates improved transferability of slot representations.

## Abstract

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.