A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents

TL;DR

A novel multi-label multi-class intent detection dataset (MLMCID-dataset) curated from existing benchmark datasets is introduced and a pointer network-based architecture (MLMCID) is proposed to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets.

Abstract

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.

Figures (4)

• Figure 1: Examples of multi-label multi intent datasets (SNIPS, Facebook and BANKING)
• Figure 2: Pointer Network Based multi-label, multi-class intent detection (MLMCID) architecture
• Figure 3: By RoBERTa based pointer network (PNM) model in MLMCID
• Figure 4: Examples in MLMCID Dataset