A Pointer Network-based Approach for Joint Extraction and Detection of
  Multi-Label Multi-Class Intents
Authors: name: Ankan Mullick, name: Sombit Bose, name: Abhilash Nandy, name: Gajula Sai Chaitanya, name: Pawan Goyal

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 multi-lingual 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.