Deep neural networks are susceptible to overfitting with noisy labels. Existing meta-learning-based methods for learning with noisy labels require additional clean validation data, resulting in high annotation costs and limiting their general applicability. Moreover, these methods only use local gradient information to approximately optimize the model parameters. In this paper, we propose a novel meta label corrector to address the above two issues. Our method is based on two core points: monitoring training dynamics through the meta-entropy value to construct a proxy clean validation dataset, and precise parameter optimization through a new combined bi-level optimization mechanism. Compared with state-of-the-art noisy label learning methods, comprehensive experiments confirm the superiority of our method in both image recognition and text classification with different label noise types and ratios.

Abstract The multiturn dialogue system has been prevalently used in e‐commerce websites and modern information systems, which significantly improves the efficiency of problem solving and further promotes the service quality. In a multiturn dialogue system, the problem of intention classification is a core task, as the intention of a customer is the basis of subsequent problems handling. However, traditional related methods are unsuitable for the classification of multiturn dialogues. Because traditional methods do not distinguish the importance of each sentence and concatenate all sentences in the text, which is likely to generate a model with low prediction accuracy. In this paper, we propose a method of multiturn dialogue classification based on key sentences mining. We design a keywords extraction algorithm, mining key sentences from the dialogue text. We propose an algorithm finishing the computation of the weights of each sentence. According to the sentence weight and the sentence vector, the dialogue text is transformed to a dialogue vector. The dialogue text is classified by a classifier, and the input is the dialogue vector. We conducted sufficient experiments on a real‐world dataset, evaluating the performance of the proposed method. The experimental results show that our method outperforms the related methods on a series of evaluation metrics.

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., Different Error Costs (DEC) and Fuzzy SVM for Class Imbalance Learning (FSVM-CIL). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we proposeATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples.ATECdistinguishes itself from all existing parameter tuning strategies by two main features: (1) it can evaluate how effective an error cost is in terms of classification accuracy; and (2) it changes the error cost in the right direction if it is not effective. Extensive experiments show that compared with the state-of-art methods, SVMs that are equipped withATECcan not only obtain comparable improvements in terms of F1 score of minority class, area under the precision-recall curve (AUC-PR) and area under the ROC curve (AUC-ROC) scores, but also outperform the grid-search parameter tuning strategy by two orders of magnitude in terms of the training time when a high F1 score is required.