title: A Pointer Network-based Approach for Joint Extraction and Detection of
    Multi-Label Multi-Class Intents
summary: 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.
paper_authors._id: 67248986478a5959135c9bd0
paper_authors.name: Ankan Mullick
paper_authors._id: 67248986478a5959135c9bd1
paper_authors.name: Sombit Bose
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-16T19:47:10.078Z
paper_authors._id: 65180f12fff98a48b2c61bc3
paper_authors.avatarUrl: /avatars/631ec6bf39d863c5e7dcfcf048258519.svg
paper_authors.fullname: Sombit Bose
paper_authors.type: user
paper_authors.user: sbcs-sombit
paper_authors._id: 67248986478a5959135c9bd2
paper_authors.name: Abhilash Nandy
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-03T14:21:52.077Z
paper_authors._id: 5f89da6c5d083370c711f37c
paper_authors.avatarUrl: /avatars/c2f17a4a636973817fd5da2ae6dbaac3.svg
paper_authors.fullname: Abhilash Nandy
paper_authors.type: user
paper_authors.user: abhi1nandy2
paper_authors._id: 67248986478a5959135c9bd3
paper_authors.name: Gajula Sai Chaitanya
paper_authors._id: 67248986478a5959135c9bd4
paper_authors.name: Pawan Goyal
paper_title: A Pointer Network-based Approach for Joint Extraction and Detection of
    Multi-Label Multi-Class Intents
paper_summary: 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.
paper_ai_summary: A pointer network-based architecture improves multi-intent and multi-span intent detection in task-oriented dialogue systems using a new multi-lingual dataset.
paper_ai_keywords: intent detection dialogue systems intent spans multi-intents pointer network MLMCID-dataset sextuplets F1-score