Dice Loss Proposed for Data-Imbalanced NLP Tasks

A research paper proposes using Dice loss as an alternative to standard cross-entropy for natural language processing tasks that suffer from severe data imbalance. This approach, based on the Sorensen-Dice coefficient or Tversky index, aims to balance the importance of false positives and false negatives, making it more resilient to imbalanced datasets. The paper also introduces a method for dynamically weighting training examples to reduce the impact of easy-negative examples, showing improved performance on various NLP tasks including part-of-speech tagging and named entity recognition. AI

IMPACT This research offers a novel approach to improve model performance on NLP tasks with imbalanced datasets, potentially leading to more accurate systems in areas like named entity recognition.

RANK_REASON The item is an academic paper detailing a new methodology for NLP tasks. [lever_c_demoted from research: ic=1 ai=1.0]

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• CoNLL03
• cross entropy
• CTB5
• CTB6
• Dice loss
• natural language processing
• OntoNotes4.0
• OntoNotes5.0
• Sorensen-Dice coefficient
• Tversky index
• UD1.4

• paper
• other

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