Novel Intent Detection and Active Learning Based Classification (Student Abstract) Article Swipe
Novel intent class detection is an important problem in real world scenario for conversational agents for continuous interaction. Several research works have been done to detect novel intents in a mono-lingual (primarily English) texts and images. But, current systems lack an end-to-end universal framework to detect novel intents across various different languages with less human annotation effort for mis-classified and system rejected samples. This paper proposes NIDAL (Novel Intent Detection and Active Learning based classification), a semi-supervised framework to detect novel intents while reducing human annotation cost. Empirical results on various benchmark datasets demonstrate that this system outperforms the baseline methods by more than 10% margin for accuracy and macro-F1. The system achieves this while maintaining overall annotation cost to be just ~6-10% of the unlabeled data available to the system.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v37i13.27003
- https://ojs.aaai.org/index.php/AAAI/article/download/27003/26775
- OA Status
- diamond
- Cited By
- 4
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382318818
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382318818Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v37i13.27003Digital Object Identifier
- Title
-
Novel Intent Detection and Active Learning Based Classification (Student Abstract)Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-06-26Full publication date if available
- Authors
-
Ankan MullickList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v37i13.27003Publisher landing page
- PDF URL
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https://ojs.aaai.org/index.php/AAAI/article/download/27003/26775Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/27003/26775Direct OA link when available
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Annotation, Computer science, Margin (machine learning), Benchmark (surveying), Artificial intelligence, Class (philosophy), Machine learning, Baseline (sea), Natural language processing, Geography, Geology, Geodesy, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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