Towards Zero-Shot Frame Semantic Parsing for Domain Scaling

TL;DR

This paper introduces a zero-shot semantic parsing method for dialogue systems that uses slot descriptions to bootstrap new domains without labeled data, improving domain scalability.

Contribution

It proposes a deep learning approach leveraging slot descriptions for zero-shot domain adaptation in slot filling, reducing reliance on annotated datasets.

Findings

Significantly improves slot filling in new domains without labeled data.

Effective in low-data scenarios, outperforming traditional methods.

Demonstrates potential for scalable domain expansion in dialogue systems.

Abstract

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.