# MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification
## 1 Introduction  
With the increase in model parameters, the fitting capacity of neural networks becomes stronger. However, when the joint distribution of the training set samples and labels differs from that of the evaluation/testing set, the training set is considered biased [1]. In the case of biased data, deep neural networks tend to overfit [2], leading to a decline in model performance. Thus, effectively utilizing biased data can reduce the high cost of data re-collection or annotation. Class imbalance [3] refers to a situation where the number of samples in one or more classes is significantly different from the number of samples in other classes. This situation is prevalent in many real-world problems, such as spam email classification and telephone fraud detection. Class imbalance can affect the performance of classifiers, causing them to predict the majority class, which would achieve decent accuracy without truly learning the essence of the problem. However, such training does not serve the purpose of building a reliable classifier, as the model's generalization ability is compromised, leading to poor performance in real-world applications. Corrupted labels [4] refer to instances where the labels in the training data do not match the true labels. This issue often arises during data collection or annotation [5] and may result from automated annotation, non-expert labeling, or adversarial label tampering [6]. The existence of corrupted labels in the dataset can negatively impact the model's performance [7], as it learns from erroneous information. In the real world, both types of bias often coexist and influence each other. A dataset may suffer from class imbalance, which, in turn, could lead to the presence of corrupted labels. Additionally, due to the bias in sample distribution, the model may more easily learn features that favor the majority class, neglecting features from other classes, furtherexacerbating the class imbalance and corrupted label issues. Therefore, research focusing on effectively handling and utilizing class imbalanced data and corrupted labels is crucial [1; 8]. Currently, many studies have attempted to address the issue of biased data, mainly employing two methods: sample re-sampling and sample reweighting. Sample re-sampling includes over-sampling [9], under-sampling, and data augmentation. Sample reweighting, on the other hand, assigns weights to each sample based on a weighted function, adjusting the impact of each sample on the model through weighted losses. In sample reweighting, a weight function along with its associated hyperparameters during model training is predefined in advance to assign different weights to samples based on their loss values, reflecting the significance of each sample in the training set. By utilizing importance weighting, the negative impact of biased samples on the model can be reduced. However, the bias type in the data is unknown. Traditional weighting methods tend to assign high weights to samples with small loss to reduce the misleading effect of label noise samples on the model when label noise exists [10]. Conversely, another scenario might emerge where high loss samples are assigned high weights, as these large loss samples could belong to minority classes and need to be forcefully learned by the classifier as hard boundary samples [11]. As a result, these weighting methods fail to accommodate both scenarios effectively. In addition, on the one hand, bias types in the data can be diverse, and the degree of bias is difficult to predict. On the other hand, the utilization of bias data's inherent information in the reweighting method is not high enough to effectively enhance the discrimination between samples. Meta-learning refers to enabling models to \"learn to learn\" and quickly adapt to new tasks with minimal training data. In recent years, the use of meta-learning has demonstrated high generalization in meta-neural networks across different tasks [12], acquiring more generalized weights than manual hyperparameter tuning [13].