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DeepOpen framework achieves high accuracy on banking and intent classification tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced DeepOpen, a framework for decision-oriented intent classification tailored for specific datasets like BANKING77 and CLINC150. The system achieved a high accuracy of 94.12% on BANKING77 using a single English classifier with sharpness-aware minimization (SAM). For CLINC150, a probability fusion of a Laya specialist and DeBERTa-v3-large classifiers reached 98.02% accuracy. The paper details the configurations, highlights performance metrics, and discusses potential selection effects and historical execution-path differences rather than claiming a novel algorithmic advantage. AI

IMPACT This research demonstrates improved intent classification accuracy on specific datasets, potentially benefiting applications requiring nuanced user understanding.

RANK_REASON The cluster contains an academic paper detailing a new framework and its performance on specific datasets. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepOpen framework achieves high accuracy on banking and intent classification tasks

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The cluster contains an academic paper detailing a new framework and its performance on specific datasets. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shuang Song, Muhammad Luqman Naseem, Zhongqiao Yuan ·

    DeepOpen: Decision-Oriented Intent Classification on BANKING77 and CLINC150

    arXiv:2610.31104v2 Announce Type: replace Abstract: We present DeepOpen, a decision-Oriented intent-classification framework with dataset- specific systems for BANKING77 and CLINC150. On BANKING77, a single English classi- fier continued with sharpness-aware minimization (SAM) ac…

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