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Entity Graph Based Dialogue State Tracking Model with Data Collection and Augmentation for Spoken Conversation
http://doi.org/10.5626/JOK.2022.49.10.891
As a part of a task-oriented dialogue system, dialogue state tracking is a task for understanding the dialogue and extracting user’s need in a slot-value form. Recently, Dialogue System Track Challenge (DSTC) 10 Track 2 initiated a challenge to measure the robustness of a dialogue state tracking model in a spoken conversation setting. The released evaluation dataset has three characteristics: new multiple value scenario, three-times more entities, and utterances from automatic speech recognition module. In this paper, to ensure the model’s robust performance, we introduce an extraction-based dialogue state tracking model with entity graph. We also propose to use data collection and template-based data augmentation method. Evaluation results prove that our proposed method improves the performance of the extraction-based dialogue state tracking model by 1.7% of JGA and 0.57% of slot accuracy compared to baseline model.
Training Data Augmentation Technique for Machine Comprehension by Question-Answer Pairs Generation Models based on a Pretrained Encoder-Decoder Model
http://doi.org/10.5626/JOK.2022.49.2.166
The goal of Machine Reading Comprehension (MRC) research is to find answers to questions in documents. MRC research requires large-scale, high-quality data. However, individual researchers or small research institutes have limitations in constructing them. To overcome the limitations, in this paper, we propose an MRC data augmentation technique using a pre-training language model. This MRC data augmentation technique consists of a Q&A pair generation model and a data validation model. The Q&A pair generation model consists of an answer extraction model and a question generation model. Both models are constructed by fine-tuning the BART model. The data validation model is added to increase the reliability of the augmented data. It is used to verify the generated augmented data. The validation model is used by fine-tuning the ELECTRA model as an MRC model. To see the performance improvement of the MRC model through the data augmentation technique, we applied the data augmentation technique to KorQuAD v1.0 data. As a result of the experiment, compared to the previous model, the Exact Match(EM) Score increased up to 7.2 and the F1 Score increased up to 5.7.
Data Augmentation for Image based Parking Space Classification Deep Model
http://doi.org/10.5626/JOK.2022.49.2.126
A parking occupancy state determination system using an ultrasonic sensor or a camera is mainly used in indoor parking lots. However, in the case of an outdoor parking lot, there is a limit to the introduction of these systems due to the high installation cost and accuracy problems. In addition, the application of deep learning is restricted because it is difficult to obtain representative learning data due to diverse lighting conditions, camera positions, and features. In this paper, we analyzed the effect of augmentation techniques on the performance of a deep model for parking status classification in such a data shortage situation. To this end, the parking area images were classified by situations. Four augmentation techniques were applied to the training of ResNet, EfficientNet, and MobileNet. Based on performance evaluation, the accuracy was improved by up to 5.2%, 8.67%, and 15.44%p in the case of mixup, stopper, and rescaling methods, respectively. On the other hand, in the case of center crop, which was known to have performance improvement in other studies, the accuracy decreased by an average of 4.86%p.
Semi-Supervised Learning Exploiting Robust Loss Function for Sparse Labeled Data
http://doi.org/10.5626/JOK.2021.48.12.1343
This paper proposes a semi-supervised learning method which uses data augmentation and robust loss function when labeled data are extremely sparse. Existing semi-supervised learning methods augment unlabeled data and use one-hot vector labels predicted by the current model if the confidence of the prediction is high. Since it does not use low-confidence data, a recent work has used low-confidence data in the training by utilizing robust loss function. Meanwhile, if labeled data are extremely sparse, the prediction can be incorrect even if the confidence is high. In this paper, we propose a method to improve the performance of a classification model when labeled data are extremely sparse by using predicted probability, instead of one hot vector as the label. Experiments show that the proposed method improves the performance of a classification model.
Data Augmentation Methods for Improving the Performance of Machine Reading Comprehension
Sunkyung Lee, Eunseong Choi, Seonho Jeong, Jongwuk Lee
http://doi.org/10.5626/JOK.2021.48.12.1298
Machine reading comprehension is a method of understanding the meaning and performing inference over a given text by computers, and it is one of the most essential techniques for understanding natural language. The question answering task yields a way to test the reasoning ability of intelligent systems. Nowadays, machine reading comprehension techniques performance has significantly improved following the recent progress of deep neural networks. Nevertheless, there may be challenges in improving performance when data is sparse. To address this issue, we leverage word-level and sentence-level data augmentation techniques through text editing, while minimizing changes to the existing models and cost. In this work, we propose data augmentation methods for a pre-trained language model, which is most widely used in English question answering tasks, to confirm the improved performance over the existing models.
Improving Low Resource Chest X-ray Classification Accuracy by Combining Data Augmentation and Weakly Supervised Learning
http://doi.org/10.5626/JOK.2021.48.9.1027
Deep learning-based medical image analysis technology has been developed to the extent that it shows an accuracy surpassing the ability of a human radiologist. However, labeling sample data for use in learning medical images requires human experts and a great deal of time and expense. In addition, the training data for medical images has an unbalanced data distribution in many cases. For example, in the case of the ChestX-ray14 dataset, the difference between the number of data for infiltration and hernia is about 87 times. In this study, we proposed a method that combines the data augmentation algorithm Mixup and weakly supervised learning to improve the performance of data-imbalanced chest X-ray classification. The proposed method is to apply Mixup algorithm to a small number of labeled data and a large number of unlabeled data to alleviate data imbalance and performs curriculum learning that effectively utilizes the unlabeled data while cycling through the teacher model and the student model. Experimental results in an environment with a small number of labeled data and a large number of unlabeled data that can be considered in the medical field showed that the classification performance was improved by combining data augmentation and weakly supervised learning and that the cyclic curriculum learning was effective.
Facial Emotion Recognition Data Augmentation using Generative Adversarial Network
http://doi.org/10.5626/JOK.2021.48.4.398
The facial emotion recognition field of computer vision has recently been identified to demonstrate meaningful results through various neural networks. However, the major datasets of facial emotion recognition have the problem of “class imbalance,” which is a factor that degrades the accuracy of deep learning models. Therefore, numerous studies have been actively conducted to solve the problem of class imbalance. In this paper, we propose “RDGAN,” a facial emotion recognition data augmentation model that uses a GAN to solve the class imbalance of the FER2013 and RAF_single that are used as facial emotion recognition datasets. RDGAN is a network that generates images suitable for classes by adding expression discriminators based on the image-to-image translation model between the existing images as compared to the prevailing studies. The dataset augmented with RDGAN showed an average performance improvement of 4.805%p and 0.857%p in FER2013 and RAF_single, respectively, compared to the dataset without data augmentation.
ChannelAug: A New Approach to Data Augmentation for Improving Image Classification Performance in Uncertain Environments
Hyeok Yoon, Soohan Kang, Ji-Hyeong Han
http://doi.org/10.5626/JOK.2020.47.6.568
We propose a new data augmentation method that works by separating the RGB channels of an image to improve image classification ability in uncertain environments. Many data augmentation methods, using technique such as flipping and cropping, have been used to improve the image classification ability of models. while these data augmentation methods have been effective in improving image classification, they have unperformed in uncertain conditions. To solve this problem, we propose the ChannelSplit that separates and reassembles the RGB channels of an image, along with the Mix ChannelSplit, that adopts the concept of MixUp[1,2] to express more diversity. In this paper, the proposed ChannelSplit and Mix ChannelSplit are called ChannelAug because they only utilize channels and do not perform any other image operations. Also, we compare ChannelAug to other image augmentation methods to prove it enhances robustness and uncertainty measures on image classification.
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