Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding

TL;DR

This paper introduces a constrained Chain-of-Thought decoding method for relation extraction in dialogue ontologies, enhancing generalization and reducing hallucinations in large language models for ontology construction.

Contribution

It extends Chain-of-Thought decoding with constraints to improve relation extraction accuracy and robustness in dialogue ontology construction tasks.

Findings

Improved relation extraction performance on two datasets.

Enhanced generalization in transfer learning scenarios.

Reduced hallucination in large language model outputs.

Abstract

State-of-the-art task-oriented dialogue systems typically rely on task-specific ontologies for fulfilling user queries. The majority of task-oriented dialogue data, such as customer service recordings, comes without ontology and annotation. Such ontologies are normally built manually, limiting the application of specialised systems. Dialogue ontology construction is an approach for automating that process and typically consists of two steps: term extraction and relation extraction. In this work, we focus on relation extraction in a transfer learning set-up. To improve the generalisation, we propose an extension to the decoding mechanism of large language models. We adapt Chain-of-Thought (CoT) decoding, recently developed for reasoning problems, to generative relation extraction. Here, we generate multiple branches in the decoding space and select the relations based on a confidence threshold. By constraining the decoding to ontology terms and relations, we aim to decrease the risk of hallucination. We conduct extensive experimentation on two widely used datasets and find improvements in performance on target ontology for source fine-tuned and one-shot prompted large language models.