Text data augmentation using generative adversarial networks – a systematic review

Insufficient data is one of the main drawbacks in natural language processing tasks, and the most prevalent solution is to collect a decent amount of data that will be enough for the optimisation of the model. However, recent research directions are strategically moving towards increasing training examples due to the nature of the data-hungry neural models. Data augmentation is an emerging area that aims to ensure the diversity of data without attempting to collect new data exclusively to boost a model’s performance. Limitations in data augmentation, especially for textual data, are mainly due to the nature of language data, which is precisely discrete. Generative Adversarial Networks (GANs) were initially introduced for computer vision applications, aiming to generate highly realistic images by learning the image representations. Recent research has focused on using GANs for text generation and augmentation. This systematic review aims to present the theoretical background of GANs and their use for text augmentation alongside a systematic review of recent textual data augmentation applications such as sentiment analysis, low resource language generation, hate speech detection and fraud review analysis. Further, a notion of challenges in current research and future directions of GAN-based text augmentation are discussed in this paper to pave the way for researchers especially working on low-text resources

Toward Text Data Augmentation for Sentiment Analysis

A significant part of natural language processing (NLP) techniques for sentiment analysis is based on supervised methods, which are affected by the quality of data. Therefore, sentiment analysis needs to be prepared for data quality issues, such as imbalance and lack of labeled data. Data augmentation methods, widely adopted in image classification tasks, include data-space solutions to tackle the problem of limited data and enhance the size and quality of training datasets to provide better models. In this work, we study the advantages and drawbacks of text augmentation methods such as easy data augmentation, back-translation, BART, and pretrained data augmentor) with recent classification algorithms (long short-term memory, convolutional neural network, bidirectional encoder representations of transformers, support vector machine, gated recurrent units, random forests, and enhanced language representation with informative entities, that have attracted sentiment-analysis researchers and industry applications. We explored seven sentiment-analysis datasets to provide scenarios of imbalanced datasets and limited data to discuss the influence of a given classifier in overcoming these problems, and provide insights into promising combinations of transformation, paraphrasing, and generation methods of sentence augmentation. The results revealed improvements from the augmented dataset, mainly for reduced datasets. Furthermore, when balanced by augmenting the minority class, the datasets were found to have improved quality, leading to more robust classifiers. The contributions to this article include the taxonomy of NLP augmentation methods and their efficiency over several classifiers from recent research trends in sentiment analysis and related fields

CUDA: Contradistinguisher for Unsupervised Domain Adaptation

In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain. Most recent works in domain adaptation rely on an indirect way of first aligning the source and target domain distributions and then learn a classifier on a labeled source domain to classify target domain. This approach of an indirect way of addressing the real task of unlabeled target domain classification has three main drawbacks. (i) The sub-task of obtaining a perfect alignment of the domain in itself might be impossible due to large domain shift (e.g., language domains). (ii) The use of multiple classifiers to align the distributions unnecessarily increases the complexity of the neural networks leading to over-fitting in many cases. (iii) Due to distribution alignment, the domain-specific information is lost as the domains get morphed. In this work, we propose a simple and direct approach that does not require domain alignment. We jointly learn CTDR on both source and target distribution for unsupervised domain adaptation task using contradistinguish loss for the unlabeled target domain in conjunction with a supervised loss for labeled source domain. Our experiments show that avoiding domain alignment by directly addressing the task of unlabeled target domain classification using CTDR achieves state-of-the-art results on eight visual and four language benchmark domain adaptation datasets.Comment: International Conference on Data Mining, ICDM 201

Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM. We also implement GADGET, a software framework to convert any malware binary to a binary undetected by malware classifiers, using the proposed attack, without access to the malware source code.Comment: Accepted as a conference paper at RAID 201

딥러닝 기반 생성 모델을 이용한 자연어처리 데이터 증강 기법

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 이상구.Recent advances in generation capability of deep learning models have spurred interest in utilizing deep generative models for unsupervised generative data augmentation (GDA). Generative data augmentation aims to improve the performance of a downstream machine learning model by augmenting the original dataset with samples generated from a deep latent variable model. This data augmentation approach is attractive to the natural language processing community, because (1) there is a shortage of text augmentation techniques that require little supervision and (2) resource scarcity being prevalent. In this dissertation, we explore the feasibility of exploiting deep latent variable models for data augmentation on three NLP tasks: sentence classification, spoken language understanding (SLU) and dialogue state tracking (DST), represent NLP tasks of various complexities and properties -- SLU requires multi-task learning of text classification and sequence tagging, while DST requires the understanding of hierarchical and recurrent data structures. For each of the three tasks, we propose a task-specific latent variable model based on conditional, hierarchical and sequential variational autoencoders (VAE) for multi-modal joint modeling of linguistic features and the relevant annotations. We conduct extensive experiments to statistically justify our hypothesis that deep generative data augmentation is beneficial for all subject tasks. Our experiments show that deep generative data augmentation is effective for the select tasks, supporting the idea that the technique can potentially be utilized for other range of NLP tasks. Ablation and qualitative studies reveal deeper insight into the underlying mechanisms of generative data augmentation. As a secondary contribution, we also shed light onto the recurring posterior collapse phenomenon in autoregressive VAEs and, subsequently, propose novel techniques to reduce the model risk, which is crucial for proper training of complex VAE models, enabling them to synthesize better samples for data augmentation. In summary, this work intends to demonstrate and analyze the effectiveness of unsupervised generative data augmentation in NLP. Ultimately, our approach enables standardized adoption of generative data augmentation, which can be applied orthogonally to existing regularization techniques.최근 딥러닝 기반 생성 모델의 급격한 발전으로 이를 이용한 생성 기반 데이터 증강 기법(generative data augmentation, GDA)의 실현 가능성에 대한 기대가 커지고 있다. 생성 기반 데이터 증강 기법은 딥러닝 기반 잠재변수 모델에서 생성 된 샘플을 원본 데이터셋에 추가하여 연관된 태스크의 성능을 향상시키는 기술을 의미한다. 따라서 생성 기반 데이터 증강 기법은 데이터 공간에서 이뤄지는 정규화 기술의 한 형태로 간주될 수 있다. 이러한 딥러닝 기반 생성 모델의 새로운 활용 가능성은 자연어처리 분야에서 더욱 중요하게 부각되는 이유는 (1) 범용 가능한 텍스트 데이터 증강 기술의 부재와 (2) 텍스트 데이터의 희소성을 극복할 수 있는 대안이 필요하기 때문이다. 문제의 복잡도와 특징을 골고루 채집하기 위해 본 논문에서는 텍스트 분류(text classification), 순차적 레이블링과 멀티태스킹 기술이 필요한 발화 이해(spoken language understanding, SLU), 계층적이며 재귀적인 데이터 구조에 대한 고려가 필요한 대화 상태 추적(dialogue state tracking, DST) 등 세 가지 문제에서 딥러닝 기반 생성 모델을 활용한 데이터 증강 기법의 타당성에 대해 다룬다. 본 연구에서는 조건부, 계층적 및 순차적 variational autoencoder (VAE)에 기반하여 각 자연어처리 문제에 특화된 텍스트 및 연관 부착 정보를 동시에 생성하는 특수 딥러닝 생성 모델들을 제시하고, 다양한 하류 모델과 데이터셋을 다루는 등 폭 넓은 실험을 통해 딥 생성 모델 기반 데이터 증강 기법의 효과를 통계적으로 입증하였다. 부수적 연구에서는 자기회귀적(autoregressive) VAE에서 빈번히 발생하는 posterior collapse 문제에 대해 탐구하고, 해당 문제를 완화할 수 있는 신규 방안도 제안한다. 해당 방법을 생성적 데이터 증강에 필요한 복잡한 VAE 모델에 적용하였을 때, 생성 모델의 생성 질이 향상되어 데이터 증강 효과에도 긍정적인 영향을 미칠 수 있음을 검증하였다. 본 논문을 통해 자연어처리 분야에서 기존 정규화 기법과 병행 적용 가능한 비지도 형태의 데이터 증강 기법의 표준화를 기대해 볼 수 있다.1 Introduction 1 1.1 Motivation 1 1.2 Dissertation Overview 6 2 Background and Related Work 8 2.1 Deep Latent Variable Models 8 2.1.1 Variational Autoencoder (VAE) 10 2.1.2 Deep Generative Models and Text Generation 12 2.2 Data Augmentation 12 2.2.1 General Description 13 2.2.2 Categorization of Data Augmentation 14 2.2.3 Theoretical Explanations 21 2.3 Summary 24 3 Basic Task: Text Classi cation 25 3.1 Introduction 25 3.2 Our Approach 28 3.2.1 Proposed Models 28 3.2.2 Training with I-VAE 29 3.3 Experiments 31 3.3.1 Datasets 32 3.3.2 Experimental Settings 33 3.3.3 Implementation Details 34 3.3.4 Data Augmentation Results 36 3.3.5 Ablation Studies 39 3.3.6 Qualitative Analysis 40 3.4 Summary 45 4 Multi-task Learning: Spoken Language Understanding 46 4.1 Introduction 46 4.2 Related Work 48 4.3 Model Description 48 4.3.1 Framework Formulation 48 4.3.2 Joint Generative Model 49 4.4 Experiments 56 4.4.1 Datasets 56 4.4.2 Experimental Settings 57 4.4.3 Generative Data Augmentation Results 61 4.4.4 Comparison to Other State-of-the-art Results 63 4.4.5 Ablation Studies 63 4.5 Summary 67 5 Complex Data: Dialogue State Tracking 68 5.1 Introduction 68 5.2 Background and Related Work 70 5.2.1 Task-oriented Dialogue 70 5.2.2 Dialogue State Tracking 72 5.2.3 Conversation Modeling 72 5.3 Variational Hierarchical Dialogue Autoencoder (VHDA) 73 5.3.1 Notations 73 5.3.2 Variational Hierarchical Conversational RNN 74 5.3.3 Proposed Model 75 5.3.4 Posterior Collapse 82 5.4 Experimental Results 84 5.4.1 Experimental Settings 84 5.4.2 Data Augmentation Results 90 5.4.3 Intrinsic Evaluation - Language Evaluation 94 5.4.4 Qualitative Results 95 5.5 Summary 101 6 Conclusion 103 6.1 Summary 103 6.2 Limitations 104 6.3 Future Work 105Docto

Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Similar to the basic structure of a brain, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.Comment: 83 pages, 22 figures, 9 tables, 100 reference