Training Dynamic based data filtering may not work for NLP datasets (2021)

Abstract: The recent increase in dataset size has brought about significant advances in natural language understanding. These large datasets are usually collected through automation (search engines or web crawlers) or crowdsourcing which inherently introduces incorrectly labeled data. Training on these datasets leads to memorization and poor generalization. Thus, it is pertinent to develop techniques that help in the identification and isolation of mislabelled data. In this paper, we study the applicability of the Area Under the Margin (AUM) metric to identify and remove/rectify mislabelled examples in NLP datasets. We find that mislabelled samples can be filtered using the AUM metric in NLP datasets but it also removes a significant number of correctly labeled points and leads to the loss of a large amount of relevant language information. We show that models rely on the distributional information instead of relying on syntactic and semantic representations.

## introduction modern deep learning networks are becoming deeper and powerful, and have led to significant advances in natural language processing (nlp) @xcite , computer vision @xcite , and speech processing @xcite . however, these networks rely on large labeled datasets to be effective. the creation of large labeled datasets has fueled the advances in nlp @xcite . abundant labeled data increases the likelihood of learning diverse phenomena, which in turn leads to models that generalize well @xcite . curating expert annotated datasets is very timeconsuming and costly (malik and bhardwaj, 2011) * equal contribution therefore large language datasets are usually collected through crowd-sourcing, by hiring human annotators @xcite or by crawling the web. such methods inherently introduce label noise in the resulting data. mislabelled training data is particularly problematic for deep neural networks with billions of parameters because they can overfit on the mislabelled data and achieve zero training error even on randomly assigned labels @xcite . training models with noisy labels can also lead to misclassification on easy examples during test-time @xcite . it is prohibitively costly to manually remove mislabeled samples from large datasets. hence, the need arises to create an automated pipeline to analyze and clean datasets. area under the margin (aum) metric was designed to identify and eliminate noisy data. it can be used as a plug-and-play method within the training pipeline of any classification network with minimal overhead @xcite . aum shows promising results in identifying mislabelled samples in image classification datasets. thus, in this paper, we investigate the applicability of the aum metric on text classification datasets. we make the following contributions: (i) we show that the aum metric has lower efficacy for filtering mislabelled data in nlp datasets than image datasets. (ii) we hypothesize that aum does not work as expected in nlp datasets as it did in image datasets because of the intrinsic nature of the data samples. they have high intra-class and low inter-class feature similarity @xcite , which is usually not the case in nlp datasets. we show samples from nlp datasets to corroborate our hypothesis. pleiss et al. (2020) and swayamdipta et al. (2020) , both use model behavior on each sample over the training process, also known as training dynamics, to identify mislabelled instances in classification datasets but using different metrics. for each sample, @xcite finds the difference in logit value of the assigned class (gold label) and the highest other logit value among the non-assigned classes averaged over training epochs called the area under the margin (aum) metric. they also introduce a fake class with samples having only mislabelled instances by definition to find a threshold aum value. samples with low aum scores are likely to be mislabelled and the threshold aum value is used to filter out such mislabelled instances. @xcite uses mean and standard deviation of the gold label probabilities over the training epochs, called confidence and variability scores, respectively, for each sample. they classify samples with low confidence and low variability as either mislabelled or "hard-to-learn" for the model. @xcite uses statistical methods to find annotators whose annotations differ considerably from the remaining annotators and use manual inspection to decide the verdict for samples annotated by these annotators. @xcite classifies training samples with the lowest gold label probabilities on a robust classifier as potentially mislabelled followed by their manual review for final decision. @xcite detects samples with erroneous labels using an instance-dependent noise model along with instance-based embedding to capture instance-specific label corruption. ## experiments following the recommendations from @xcite , we test the efficacy of aum on syntheticnoisy and real-world nlp datasets to identify mislabelled samples. to create synthetic-noisy datasets, we injected noise in the real-world datasets by uniformly sampling data points and flipping their labels. we run two experiments on both types of datasets. first, we discard the samples classified by aum as mislabelled; we will refer to this process as sieving. second, instead of discarding samples, we rectify the label and reuse them for training; we will refer to this process as flipping. since we train on binary-classification tasks, we flip the label of the samples which are classified as mislabelled. as noted in @xcite , the aum threshold for filtration is dataset dependent. the authors provide a simple heuristic for classifying samples as mislabelled; samples with aum lower than the overfitting is another issue that is facilitated by indiscriminately removing samples with low aum as the proportion of easy-to-learn samples is increased in the filtered dataset @xcite . ## results & analysis in we report on the applicability of aum on nlp datasets. aum does help in identifying mislabelled samples available to some extent, but sieving these samples indiscriminately removes large amounts of relevant language information. we hypothesize that the reason aum works well in image datasets is because of the intrinsic nature of the data samples, i.e., data samples in image datasets have high intra-class and less inter-class feature similarity whereas in nlp datasets, data samples have high inter-class feature similarity as seen in