A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents
Ankan Mullick, Sombit Bose, Abhilash Nandy, G Sai Chaitanya, Pawan Goyal · 2024
In task-oriented dialogue systems, intent detection is crucial for interpreting user queries and providing appropriate responses.Existing research primarily addresses simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents and extracting different intent spans.Additionally, there is a notable absence of multilingual, multi-intent datasets.This study addresses three critical tasks: extracting multiple intent spans from queries, detecting multiple intents, and developing a multilingual multi-label intent dataset.We introduce a novel multi-label multi-class intent detection dataset (MLMCID-dataset) curated from existing benchmark datasets.We also propose a pointer network-based architecture (MLMCID) to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets.Comprehensive analysis demonstrates the superiority of our pointer network based system over baseline approaches in terms of accuracy and F1-score across various datasets.