Ontology-Enhanced Slot Filling

TL;DR

This paper introduces an ontology-enhanced method for slot filling in multi-domain dialog systems, improving accuracy by matching entities across dialogue turns and encoding them as additional inputs to a BERT-based tracker.

Contribution

It proposes a novel ontology-based approach that enhances dialogue state tracking by accumulating and encoding entity matches, addressing input capacity limitations.

Findings

Improved joint goal accuracy from 52.63% to 53.91%.

Enhanced slot F1 score from 91.64% to 92%.

Demonstrated effectiveness on MultiWOZ 2.1 dataset.

Abstract

Slot filling is a fundamental task in dialog state tracking in task-oriented dialog systems. In multi-domain task-oriented dialog system, user utterances and system responses may mention multiple named entities and attributes values. A system needs to select those that are confirmed by the user and fill them into destined slots. One difficulty is that since a dialogue session contains multiple system-user turns, feeding in all the tokens into a deep model such as BERT can be challenging due to limited capacity of input word tokens and GPU memory. In this paper, we investigate an ontology-enhanced approach by matching the named entities occurred in all dialogue turns using ontology. The matched entities in the previous dialogue turns will be accumulated and encoded as additional inputs to a BERT-based dialogue state tracker. In addition, our improvement includes ontology constraint checking and the correction of slot name tokenization. Experimental results showed that our ontology-enhanced dialogue state tracker improves the joint goal accuracy (slot F1) from 52.63% (91.64%) to 53.91% (92%) on MultiWOZ 2.1 corpus.