Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding
This work addresses the challenge of building task-specific ontologies for dialogue systems, which are often manually constructed, by offering an automated approach that could reduce labor and enhance scalability, though it appears incremental as it adapts existing methods to a specific domain.
The authors tackled the problem of automating relation extraction for dialogue ontologies by proposing a constrained Chain-of-Thought decoding method for large language models, resulting in improved performance on target ontologies for both fine-tuned and one-shot prompted models.
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.