Dice Loss for Data-imbalanced NLP Tasks (1911.02855v3)

Abstract: Many NLP tasks such as tagging and machine reading comprehension are faced with the severe data imbalance issue: negative examples significantly outnumber positive examples, and the huge number of background examples (or easy-negative examples) overwhelms the training. The most commonly used cross entropy (CE) criteria is actually an accuracy-oriented objective, and thus creates a discrepancy between training and test: at training time, each training instance contributes equally to the objective function, while at test time F1 score concerns more about positive examples. In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks. Dice loss is based on the Sorensen-Dice coefficient or Tversky index, which attaches similar importance to false positives and false negatives, and is more immune to the data-imbalance issue. To further alleviate the dominating influence from easy-negative examples in training, we propose to associate training examples with dynamically adjusted weights to deemphasize easy-negative examples.Theoretical analysis shows that this strategy narrows down the gap between the F1 score in evaluation and the dice loss in training. With the proposed training objective, we observe significant performance boost on a wide range of data imbalanced NLP tasks. Notably, we are able to achieve SOTA results on CTB5, CTB6 and UD1.4 for the part of speech tagging task; SOTA results on CoNLL03, OntoNotes5.0, MSRA and OntoNotes4.0 for the named entity recognition task; along with competitive results on the tasks of machine reading comprehension and paraphrase identification.

• The paper introduces a self-adjusting Dice loss that aligns with the F1 metric to better address data imbalance in NLP tasks.
• It demonstrates significant F1 score improvements over cross-entropy loss through experiments in POS tagging, entity recognition, and similar tasks.
• The study highlights the method's capacity to prioritize reduction of false negatives while noting its reduced efficacy in accuracy-focused scenarios.

An Analysis of Dice Loss for Data-imbalanced NLP Tasks

The paper entitled "Dice Loss for Data-imbalanced NLP Tasks" addresses a significant challenge in natural language processing: the inefficacy of the cross-entropy loss function in scenarios with pronounced data imbalance. The authors propose an alternative, the self-adjusting Dice loss (DSC), demonstrating its effectiveness across several NLP tasks where traditional loss functions like cross-entropy (CE) fall short.

Overview of Contributions

The primary contribution of this work is the adaptation and application of Dice loss, traditionally utilized in image segmentation, to NLP. The authors propose modifications to better align the loss function with the F1 evaluation metric, which is more attuned to the balance between precision and recall compared to accuracy. This adjustment is crucial for tasks that inherently involve imbalanced datasets, such as sequence tagging, where negative examples heavily outnumber positive ones.

By downweighting losses from correctly classified negative samples and adjusting as the probability of the correct class nears one, DSC effectively redirects the optimization focus towards reducing false negatives. The paper substantiates the improvement of DSC over CE in boosting the F1 score through extensive experimentation across multiple tasks, setting new state-of-the-art performance benchmarks.

Experimental Evidence

The empirical validation of their approach covers a range of NLP tasks, such as Chinese part-of-speech tagging and entity recognition in both English and Chinese contexts. Notably, DSC exhibits a marked enhancement in F1 scores when applied to these tasks using a BERT-based model. Furthermore, the robustness of DSC against varying degrees of data imbalance is quantitatively demonstrated, particularly in synthetic paraphrase identification datasets with negative biases.

However, the paper also acknowledges the limitations of DSC in scenarios where accuracy might be a more relevant metric than F1. This point is highlighted through controlled experiments showing DSC's diminished efficacy in tasks evaluated strictly by accuracy, as opposed to F1.

Technical Considerations and Implications

The novelty of introducing a decay factor to the Dice loss for NLP tasks is presented as a straightforward yet empirically powerful modification. While this technique has seen application in other domains, its specific adaptation here underscores a potential shift in how loss functions might be selected and tuned based on the evaluation metric of interest, especially in imbalanced data settings.

The authors also engage critically with the broader implications of choosing loss functions that mirror evaluation metrics. This perspective raises relevant questions about the balance between overfitting to metrics and optimally aligning model training objectives with task-specific performance goals.

Limitations and Future Directions

While the paper establishes a strong case for DSC's applicability, there are suggested avenues for further exploration. Notably, a deeper analysis of dynamic weighting functions besides DSC could be pursued, potentially offering insights into adapting such frameworks to other loss functions like focal loss. Additionally, the paper suggests the necessity for broader testing across diverse datasets and imbalances to fully validate the generalizability and advantages of the proposed method.

In conclusion, this research contributes meaningfully to the ongoing discourse on loss functions suitable for imbalanced NLP tasks, providing empirical evidence for a more nuanced consideration of task-specific optimization strategies. The methodological simplicity and demonstrated efficacy of DSC make it a compelling tool for NLP practitioners, albeit with recognized areas for further theoretical and experimental exploration.