Unsupervised Approach for Knowledge-Graph Creation from Conversation: The Use of Intent Supervision for Slot Filling
Zishan Ahmad, Asif Ekbal, Shubhashis Sengupta, Anutosh Maitra, Roshni Ramnani, Pushpak Bhattacharyya · 2021
In this paper, we propose an unsupervised approach for knowledge graph (KG) creation from conversational data. We make use of intent classification and slot-filling, the two important components of any dialogue agent, exploit their interconnectedness, and finally construct a KG. We build a supervised intent classifier to extract the intent classes, and then on top of this we run our occlusion based slot-information extraction algorithm. Our algorithm is able to make use of supervised training of intent classifiers for extracting the relevant slot-information in an unsupervised way. To test the effectiveness of our system, we perform both automatic and manual evaluation of our intent-classifier and slot-filling system on three dialog datasets. Finally, we construct a knowledge graph from the dialogue conversation using an algorithm that makes use of our occlusion based slot-information extraction module. Empirical evaluation shows that our occlusion based method is able to successfully extract slot information from conversations, resulting in a high-quality KG.