1 Introduction
Deep Neural Networks (DNNs) have led to significant advances in the fields of computer vision
He et al. (2016), speech processing Graves et al. (2013)and natural language processing
Kim (2014); Young et al. (2018); Devlin et al. (2018). To be effective, supervised DNNs rely on large amounts of carefully labeled training data. However, it is not always realistic to assume that example labels are clean. Humans make mistakes and, depending on the complexity of the task, there may be disagreement even among expert labelers. To support noisy labels in data, we need new training methods that can be used to train DNNs directly from the corrupted labels to significantly reduce human labeling efforts. Zhu and Wu Zhu and Wu (2004)perform an extensive study on the effect of label noise on classification performance of a classifier and find that noise in input features is less important than noise in training labels.
In this work, we add a noise model layer on top of our target model to account for label noise in the training set, following Jindal et al. (2016); Sukhbaatar et al. (2014). We provide extensive experiments on several text classification datasets with artificially injected label noise. We study the effect of two different types of label noise; Uniform label flipping (Uni), where a clean label is swapped with another label sampled uniformly at random; and Random label flipping (Rand) where a clean label is swapped with another label from the given number of labels sampled randomly over a unit simplex.
We also study the effect of different initialization, regularization, and batch sizes when training with noisy labels. We observe that proper initialization and regularization helps the noise model learn to be robust to even extreme amounts of noise. Finally, we use lowdimensional projections of the features of the training examples to understand the effectiveness of the noise model.
The rest of the paper is organized as follows. Section 2 discusses the various approaches in literature to handle label noise. In Section 3, we describe the problem statement along with the proposed approach. We describe the experimental setup and datasets in Section 4. We empirically evaluate the performance of the proposed approach along with the discussion in Section 5 and finally conclude our work in Section 6.
2 Related Work
Learning from label noise is a widely studied problem in the classical machine learning setting. Earlier works
Brodley and Friedl (1999); Rebbapragada and Brodley (2007); Manwani and Sastry (2013) consider learning from noisy labels for a wide range of classifiers including SVMs Natarajan et al. (2013) and fisher discriminants Lawrence (2001). Traditional approaches handle label noise by detecting and eliminating the corrupted labels. More details about these approaches can be found in Frénay and Verleysen (2014).Recently, DNNs have made huge gains in performance over traditional methods on large datasets with very clean labels. However large realworld datasets often contain label errors. A number of works have attempted to address this problem of learning from corrupted labels for DNNs. These approaches can be divided into two categories; attempts to mitigate the effect of label noise using auxiliary clean data, and attempts to learn directly from the noisy labels.
Presence of auxiliary clean data: This line of research exploits a small, clean dataset to correct the corrupted labels. For instance, Li et al. (2017)
learn a teacher network with clean data to reweight a noisy label with a soft label in the loss function. Similarly,
Veit et al. (2017) use the clean data as a label correction network. One can use this auxiliary source of information to do inference over latent clean labels Vahdat (2017). Further, Yao et al. (2018) models the auxiliary trustworthiness of noisy image labels to alleviate the effect of label noise. Though these methods show very promising results, the absence of clean data in some situations might hinder the applicability of these methods.Learning directly from noisy labels: This research directly learns from the noisy labels by designing a robust loss function, or by modeling the latent labels. For instance, Reed et al. (2014), apply bootstrapping to the loss function to have consistent label prediction for similar images. Similarly, Joulin et al. (2016) alleviate the label noise effect by adequately weighting the loss function using the sample number. Jiang et al. (2017) propose a sequential metalearning model that takes in a sequence of loss values and outputs the weights for the labels. Ghosh et al. (2017) further explores the conditions on loss functions such that the loss function is noise tolerant.
A number of approaches learn the transition from latent labels to the noisy labels. For example, Mnih and Hinton (2012) propose a noise adaptation framework for symmetric label noise. Based on this work, several other works Sukhbaatar et al. (2014); Jindal et al. (2016); Patrini et al. (2017); Han et al. (2018)
account for the label noise by learning a noisy layer on top of a DNN where the learned transition matrix represents the label flip probabilities. Similarly,
Xiao et al. (2015) propose a probabilistic image conditioned noise model. Azadi et al. (2015) proposed an image regularization technique to detect and discard the noisy labeled images. Other approaches include building two parallel classifiers Misra et al. (2016) where one classifier deals with image recognition and the other classifier models human’s reporting bias.All of these approaches have targeted image classification. In this work, we propose a framework for learning from noisy labels for text classification using a DNN architecture. Similar to Sukhbaatar et al. (2014); Jindal et al. (2016); Patrini et al. (2017), we append a nonlinear processing layer on top of this architecture to model the label noise. This layer helps the base architecture to learn better representations, even in the presence of label noise. We empirically show that, for better classification performance, the knowledge of noise transition matrix is not needed. Instead, the process forces the DNN to learn better sentence representations.
3 Problem Statement
In a supervised text classification setting where is a dimensional word embedding of the th word in a sentence of length
(padded wherever necessary), we represent the sample as an temporal embedding matrix
which belongs to one of the classes. Let the noisefree training set be denoted by where represents the category of th sample,is the total number of training samples, and there is an unknown joint distribution
on the sample/label pairs. This temporal representation of a sample is fed as input to a classifier on the training set with sample categories . However, as mentioned in Section 2, we cannot access the true noisefree samples labels and instead, observe noisy labels corrupted by an unknown noise distribution. Let this noisy training set be denoted bywhere represents the corrupted label for the sentence . In this work, we suppose the label noise is classconditional, where the noisy label depends only on the true label , but not on the input or any other labels or . Under this model, the label noise is characterized by the conditional distribution which we describe via the
columnstochastic matrix
, parameterized by a matrix .In our experiments, we artificially inject label noise into the training and validation sets. We fix the noise distribution and, for a training sample, we generate a noisy label by drawing i.i.d from this noise distribution . However, we do not alter the test labels.
Though the proposed approach works for any noise distribution, for this study, we focus on two different types of label flip distributions. We use a noise model parameterized by the overall probability of a label error, denoted by . For a noise level , we set the noise distribution matrix
(1) 
and we call it Uniform label flip noise model. Here,
represents the identity matrix and
denotes the allones matrix. Similarly, we describe the random label flip noise model as(2) 
where is the identity matrix, and is a matrix with zeros along the diagonal and remaining entries of each column are drawn uniformly and independently from the dimensional unit simplex. The label error probability for each class is
, while the probability distribution
within the erroneous classes is drawn uniformly at random.Our objective is to train a classifier on the noisy labeled sample categories on the training set such that it jointly makes accurate predictions of the true label and learns the noise transition matrix simultaneously, given . For the noisy dataset , it is straightforward to train a classifier that predicts the noisy labels using conditional distribution for the noisy labeled input sentence :
(3) 
One can learn the classifier associated with via standard training on the noisy set . To predict the clean labels by learning the conditional distribution requires more effort, as we cannot extract the “clean” classifier from the noisy classifier when the label noise distribution is unknown.
3.1 Proposed Framework
We refer to the DNN model without the final layer as the base model or network without noise model (WoNM). This model, along with the nonlinear layer, is trained via backpropagation on the noisy training dataset. The nonlinear processing layer in the noise model transforms the base model outputs to match the noisy labels during the forward pass better and presents the denoised labels to the base model during the backward pass. The noise layer is parameterized by a square matrix ). At test time, we remove this learned noise model and use the output of the base model as final predictions.
We refer to the base model parameters as . The base model outputs a probability distribution over the number of categories denoted as . During the forward pass the noise model transforms this output to obtain the noisy labels as
(4) 
where represents the usual softmax operator. Note that both the equations (3) and (4) compute the probability distribution over noisy labels – our noise model does not learn a noise transition matrix. However, we assert that the knowledge of exact noise statistics is neither necessary nor sufficient for the better prediction results.
We learn the base model parameters and the noise model parameters by maximizing the log likelihood (4) over all of the training samples, minimizing the crossentropy loss:
(5) 
Similar to Sukhbaatar et al. (2014), we initialize the noise model weights to the identity matrix. Since DNNs have high capacity, we may encounter the situation when the model absorbs all the label noise and, thus, the noise model does not learn anything at all. In order to avoid this situation, and to prevent overfitting, we apply regularization to the noise model. However, we want the noise model to overfit the label noise. In the experiment section, we observe that with proper regularization and weight initialization the noise model absorbs most of the label noise. Finally, we train the entire network according to the following loss function:
(6) 
Here, is a tuning parameter and we validate the value of by repeating the experiment multiple times with multiple values over different datasets and choose the one with better classification performance. A value of works best.
4 Datasets and Experimental Setup
In this section, we empirically evaluate the performance of the proposed approach for text classification and compare our results with the other methods.
4.1 General Setting
In all the experiments, we use a publiclyavailable deep learning library
Baseline – a fast model development tool for NLP tasks Pressel et al. (2018). For all the different datasets, we choose a commonlyused, highperformance model from Kim (2014) as a base model. To examine the robustness of the proposed approach, we intentionally flip the class labels with to label noise, in other words: , and observe the effect of different types of label flipping, such as uniform (Uni) and random (Rand) label flipping, along with instancedependent label noise. For all the experiments, we use early stopping based on validation set accuracy where the class labels in validation are also corrupted.We indicate the performance of a standard deep network Without Noise model (WoNM) on the noisy label dataset. We also plot the results for the stacked Noise Model Without Regularization (NMWoRegu) and stacked Noise Model With Regularization (NMwRegu). Unless otherwise stated, in all the deep networks with the stacked noise model, we initialize the noise layer parameters as an identity matrix. We further analyze the effect of the noise layer initialization on the overall performance. We define TDwRegu as the stacked noise model with regularization, initialized with true injected noise distribution and RandwRegu as the stacked noise model with regularization, initialized randomly. We run all experiments five times and report the mean accuracy.
4.2 Datasets
Text Data 
Dataset  K  L  N  T  Type 

SST2  2  19  76961  1821  Balanced  
Trec  6  10  5000  500  Not Balanced  
AGNews  4  110K  10K  Balanced  
DbPedia  14  29  504K  70K  Balanced 
SST2 
Batch Size  50  100  
Label Flips  Random  Random  
Noise%  Clean Data  10  20  30  40  45  47  50  0  10  20  30  40  45  47  50  
WoNM  
TDwRegu01  83.29%  78.53%  74.01%  49.5%  86.88%  84.88%  85.08%  82.41%  76.09%  70.10%  58.98%  
NMWoRegu  87.28%  86.2%  55.76%  52.24%  
NMwRegu001  86.51%  85.26%  
NMwRegu01  87.78%  86.04%  85.04%  82.7%  77.43%  66.96%  61.5%  49.08%  85.10%  81.9%  76.2%  65.47%  58.92%  52.46%  
Trec 
Batch Size  10  
Label Flips  Uniform  Random  
Noise%  Clean data  10  20  30  40  50  60  70  0  10  20  30  40  50  60  70  
WoNM  
TDwRegu01  
NMWoRegu  
NMwRegu001  34.87%  
NMwRegu01  92.73%  90.8%  89.53%  88.67%  84.93%  79.67%  69.67%  52.4%  92.7%  90.33%  90.6%  86.47%  83.07%  70.93%  65.2%  
Batch Size  50  
Label Flips  Uniform  Random  
Noise%  0  10  20  30  40  50  60  70  0  10  20  30  40  50  60  70  
WoNM  
TDwRegu01  
NMWoRegu  
NMwRegu001  
NMwRegu01  92.53%  91.33%  90.27%  88.47%  83.87%  77.87%  68.73%  55.67%  92.53%  90.00%  90.2%  85.93%  82.6%  71.4%  67.33%  37.53%  
AGNews 
Batch Size  100  
Label Flips  Uniform  Random  
Noise%  0  10  20  30  40  50  60  70  0  10  20  30  40  50  60  70  
WoNM  
TDwRegu01  
NMWoRegu  77.66%  
NMwRegu001  92.62%  
NMwRegu01  92.55%  92.23%  92.2%  91.98%  91.7%  91.23%  90.54%  89.78%  92.23%  91.96%  91.69%  91.13%  90.77%  62.04%  
Batch Size  1024  
Label Flips  Uniform  Random  
Noise%  0  10  20  30  40  50  60  70  0  10  20  30  40  50  60  70  
WoNM  
TDwRegu01  
NMWoRegu  
NMwRegu001  
NMwRegu01  92.66%  92.2%  92.29%  92.09%  91.7%  91.24%  90.72%  89.88%  92.57%  92.11%  91.99%  91.57%  91.2%  90.5%  77.93%  61.12%  
DBpedia 
Batch Size  512  
Label Flips  Uniform  Random  
Noise%  Clean data  30  50  70  75  80  85  90  0  30  50  70  75  80  85  90  
WoNM  
NMWoRegu  
NMwRegu001  99.04%  98.94%  98.81%  98.61%  98.52%  98.33%  98.13%  97.53%  99.04%  98.48%  98.33%  89.00%  
NMwRegu01  99.01%  98.88%  98.72%  16.27%  
Batch Size  1024  
Label Flips  Uniform  Random  
Noise%  Clean data  30  50  70  75  80  85  90  0  30  50  70  75  80  85  90  
WoNM  
NMWoRegu  
NMwRegu001  98.97%  98.9%  98.79%  98.53%  98.50%  98.32%  98.19%  97.27%  98.97%  98.49%  98.32%  83.79%  
NMwRegu01  98.88%  98.72%  98.35%  15.94% 
Here, we describe all the text classification datasets used to evaluate the performance of the proposed approach. The base model architecture is the same for all datasets. For each set, we tune the number of filter windows and filter lengths using the development set. Along with the description, we also provide the hyperparameters we selected for each. Table 1 summarizes the basic statistic of the datasets.

SST2^{1}^{1}1http://nlp.stanford.edu/sentiment/ Socher et al. (2011): Stanford Sentiment Treebank dataset for predicting the sentiment of movie reviews. The classification task involves detecting positive or negative reviews. Using the base model with clean labels we obtain classification accuracy of . For this dataset, the base model network architecture consists of an input and embedding layer + feature windows with 100 feature maps each and dropout rate with batch size 50.

TREC^{2}^{2}2http://cogcomp.cs.illinois.edu/Data/QA/QC/ Voorhees and Tice (1999): A question classification dataset consisting of fact based questions divided into broad semantic categories. We use a sixclass version of TREC dataset. For this dataset, the base model network architecture consists of an input and embedding layer + one feature windows with 100 feature maps and dropout rate with batch size 10.

AgNews^{3}^{3}3http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html Zhang et al. (2015): A largescale, fourclass topic classification dataset. It contains approx 110K training samples. For this dataset, the base model network architecture consists of Input layer + Embedding layer + feature windows with 200 feature maps and dropout rate with batch size 100.

DBpedia3 Zhang et al. (2015): A large scale 14class topic classification dataset containing training samples per category. For this dataset, the base model network architecture consists of Input layer + Embedding layer + feature windows with 400 feature maps each and dropout rate with batch size 1024.
For all the datasets, we use Rectified Linear Units (ReLU) and fix the base model architecture. We use early stopping on dev sets for all the datasets. We run all the experiments 5 times and report the average classification accuracy in Table
2. We train all the networks endtoend via stochastic gradient descent over shuffled minibatches with the Adadelta update rule
Zeiler (2012) except for the DBpedia, where we use SGD. In order to improve base model performance, we initialize the word embedding layer with the publicly available word2vecword vectors
Mikolov et al. (2013) for all the datasets except for DBpedia, where we use GloVe embeddings Pennington et al. (2014).5 Results and Discussion
We evaluate the performance of our model in Table 2 for each datasets in the presence of uniform and random label noise and compare the performance with the base model (WoNM) as our baseline. For the other datasets, the proposed approach is significantly better than the baseline for both types of label noise. For all datasets, we observe a gain of approximately w.r.t baseline in the presence of extreme label noise. Interestingly, if we assume an oracle to determine prior knowledge of true noise distribution (TDwRegu01), it does not necessarily improve classification performance, especially for multiclass classification problems. For binary classification, using the SST2 dataset, we did observe that the noise model initialized with the true noise distribution works better than all the other models.
5.1 Effect of different regularizers
The NMwRegu01 performs better in all cases for both types of label noise. We plot the weight matrix learned by all the noise models in all the noise regimes. For brevity, we only plot the weight matrix for AGNews datasets with label noise in Fig.1. We find that regularization diffuses the diagonal weight elements and learns more smoothed offdiagonal elements which resemble the corresponding input label noise distribution in Fig. 0(d). This also means that, without regularization, the noise model has less ability to diffuse the diagonal elements which leads to poor classification performance. Therefore, we use a regularizer () to diffuse the diagonal entries.
In some cases, especially for low label noise, we find the regularization with a small penalty works better than a large penalty since, for low label noise, learning a less diffuse noise is beneficial. The proposed approach scales to a large number of label categories, as evident from the experiments on DBpedia dataset in the last row of Table .2.
5.2 Effect of different scaling factors on noise layer initialization
We initialize the noise model weights as identity matrices with gain equal to the number of classes (gain = ) for all experiments. We observe the effect of different gain values on the overall performance of the proposed network in Fig. 2. We plot the classification performance for the DBpedia dataset with random noise. For each noise model in Fig. 1(a), we find that setting the gain to works best and any other gain results in poor performance.
In Fig. 1(b) we plot the Frobenius norm of the learned noise model weights with respect to the different gain values. We find that, using the high gain initialization, the model learns a high noise model norm, resulting in poor classification performance. This finding provides support to the claim in Liao et al. (2018) that “higher capacity leads to high test errors.”
5.3 Effect of Batch size
We also observe the effect of different batch sizes on performance as described in Rolnick et al. (2017). For all datasets, we do observe small performance gains for highly nonuniform noisy labels, for instance , in Fig. 3 column 2. However, for uniform label flips, we do not observe performance gains with increasing batch size.
5.4 Instance Dependent label noise
We further investigate the performance of the proposed approach on instancedependent label noise by flipping each class labels with different noise percentages as shown in Fig. 3(a). For brevity, we present results on AGNews dataset in Fig. 4. On this type of label noise, the performance of proposed approach is far better than the baseline with a performance improvement of . The learned noise model by the proposed approach is shown in Fig. 3(b) and we show the normalized weight matrix in Fig. 3(c). We observe that the learned noise model is able to capture the input label noise statistics and is highly correlated to the input noise distribution with Pearson Correlation Coefficient .
5.5 Understanding Noise Model
TRB  TRPr  

Data(N)  WoNM  Noisy  True  NMwRegu01  Noisy  True 
SST2 (40%)  70.24  70.95  79.24  82.32  73.90  83.25 
AG (70%)  59.70  52.44  79.18  90.33  86.27  89.4 
AG (60%)  83.25  68.8  88.28  90.45  87.77  90.78 
Trec (40%)  66.80  63.4  79.0  73.40  69.6  83.2 
Trec (20%)  83.6  80.0  86.0  87.40  83.6  90.0 


In order to further understand the noise model, we first train the base model and the proposed model on noisy labels. Afterward, we collect the last fullyconnected layer’s activations for all the training samples and treat them as the learned feature representation of the input sentence. We get two different sets of feature representations, one corresponding to the base model (TRB), and the other corresponding to the proposed model (TRPr). Given these learned feature representations – the artificially injected noisy labels and the true labels of the training data – we learn two different SVMs for each model, with and without noise. For the base model, for both SVMs, we use TRB representation as inputs and train the first SVM with the true labels as targets and the second SVM with the unreliable labels as targets. Similarly, we train two SVMs for the proposed model. After training, we evaluate the performance of all the learned SVMs on clean test data in Table 3, where the 1st column represents the corresponding model performance, “Noisy” and “True” column represents the SVM performance when trained on noisy and clean labels, respectively. We run these experiments for different datasets with different label noise.
The SVM, trained on TRB and noisy labels, is very close to the base model performance (3). This suggests that the base model is just fitting the noisy labels. On the other hand, when we train an SVM on the TRPr representations with true labels as targets, the SVM achieves the proposed model performance. This means that the proposed approach helps the base model to learn better feature representations even with the noisy targets, which suggest that this noise model is learning a label denoising operator.
We analyze the representation of training samples in feature domain by plotting the tSNE embeddings Van Der Maaten (2014) of the TRB and TRPr. For brevity, we plot the tSNE visualizations for trec dataset with label noise in Fig. 5 .
For each network, we show two different tSNE plots. For example in Fig. 4(a) we plot two rows of tSNE embeddings for the proposed model. In the first row of Fig. 4(a), each training sample is represented by its corresponding true label, while in the second row (the noisy label plot) each training sample is represented by its corresponding noisy label. We observe that, as the learning process progresses, the noise model helps the base model to cluster the training samples in the feature domain. With each iteration, we can see the formation of clusters in Row 1. However, in Row 2, when the noisy labels are superimposed, the clusters are not well separated. This means that the noise model denoises the labels and presents the true labels to the base network to learn.
In Fig. 4(b), we plot two rows of tSNE embeddings of the TRB representations. It seems that the network directly learns the noisy labels. This provides further evidence to support Zhang et al. (2016)’s finding that the deep network memorizes data without knowing of true labels. In Row 2 of Fig. 4(b), we can observe that the network learns noisy features representations which can be well clustered according to given noisy labels.
6 Conclusion
In this work, we propose a framework to enable a DNN to learn better sentence representations in the presence of label noise for text classification tasks. To model the label noise, we append a nonlinear noise model on top of the base CNN architecture. With proper initialization and regularization, the noise model is able to absorb most of the label noise and helps the base model to learn better sentence representations.
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