The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of state-of-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data. We have developed two novel data augmentation techniques which generate new training examples from existing ones. First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as "small dog - dog" or "small dog - animal", for which a hypernymy relationship can be assumed. Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed. We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as WordNet. Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance.
Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection
Bar-Ilan University（巴伊兰大学）；Singapore University of Technology and Design（新加坡科技设计大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Stuttgart（斯图加特大学）
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；The Hospital for Sick Children（病童医院）；University Health Network（大学健康网络）；Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples
Dali University（大理大学）；Liuzhou Vocational & Technical College（柳州职业技术学院）；The Second Xiangya Hospital of Central South University（中南大学湘雅二医院）；Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer（湖南省肺癌早期诊断与精准治疗重点实验室）；Early-Stage Lung Cancer Center（早期肺癌中心）
Enhanced Offensive Language Detection Through Data Augmentation
Dartmouth College（达特茅斯学院）
检测社交媒体上的攻击性语言是一项重要任务。ICWSM-2020数据挑战任务2旨在使用包含10万条标记推文的众包数据集识别攻击性内容。然而，数据集存在类别不平衡，与其他类别相比，某些标签非常罕见（例如，仇恨类仅占数据的5%）。在这项工作中，我们提出了Dager（数据增强器），一个基于生成的数据增强方法，提高了不平衡和低资源数据，如攻击性语言数据集的分类性能。Dager提取给定类的词法特征，并使用这些特征来指导生成基于GPT-2的条件生成器。然后，可以将生成的文本作为增强数据添加到训练集中。我们表明，当我们使用整个数据集的1%进行训练（使用BERT进行分类）时，应用Dager可以将数据挑战的F1得分提高11%;此外，生成的数据也很好地保留了原始标签。我们在四种不同的分类器（BERT，CNN，Bi-LSTM with attention和Transformer）上测试了Dager，观察到检测的普遍改进，表明我们的方法是有效的，并且与分类器无关。
Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language
National University of Science and Technology POLITEHNICA Bucharest（布加勒斯特理工科学技术大学）；National Institute for Research and Development in Informatics (ICI)（国家信息学研究与发展研究所）；Academy of Romanian Scientists（罗马尼亚科学院）
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of Sciences（中国科学院自动化研究所模式识别国家实验室）；School of Artificial IntelligenceUniversity of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Continual Few-shot Event Detection via Hierarchical Augmentation Networks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Ant Group（蚂蚁集团）
SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；Peng Cheng Laboratory（鹏城实验室）；Central South University（中南大学）
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
Ingenuity Labs Research Institute（创新实验室研究所）；Queen’s University（女王大学）；Smith School of Business（史密斯商学院）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Hate Speech Detection using Large Language Models with Data Augmentation and Feature Enhancement
AiLECS Lab Monash University Melbourne, Australia（AiLECS实验室 墨尔本大学 澳大利亚）；School of Social Sciences Monash University Melbourne, Australia（社会科学学院 墨尔本大学 澳大利亚）
