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