Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number of text augmentation techniques have emerged in the field of Natural Language Processing (NLP) which can enrich the training data with new examples, though they are not without their caveats. For instance, simple rule-based heuristic methods are effective, but lack variation in semantic content and syntactic structure with respect to the original text. On the other hand, more complex deep learning approaches can cause extreme shifts in the intrinsic meaning of the text and introduce unwanted noise into the training data. To more reliably control the quality of the augmented examples, we introduce a state-of-the-art approach for Self-Controlled Text Augmentation (STA). Our approach tightly controls the generation process by introducing a self-checking procedure to ensure that generated examples retain the semantic content of the original text. Experimental results on multiple benchmarking datasets demonstrate that STA substantially outperforms existing state-of-the-art techniques, whilst qualitative analysis reveals that the generated examples are both lexically diverse and semantically reliable.
TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation
Peking University（北京大学）；Southern University of Science and Technology（南方科技大学）；Tongji University（同济大学）；Shanghai University of Finance and Economics（上海财经大学）；The University of Hong Kong（香港大学）
Mix and Match: Context Pairing for Scalable Topic-Controlled Educational Summarisation
Visual Intelligence Laboratory, Department of Statistics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand（科纳坎大学科学学院统计学系视觉智能实验室）；Centre for Artificial Intelligence, Department of Computer Science, University College London, London WC1E 6BT, The United Kingdom（伦敦大学学院人工智能中心计算机科学系）
Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models
III-LIDI Universidad Nacional de La Plata（III-LIDI国立拉普拉塔大学）；CDTEC, Federal University of Pelotas（CDTEC，联邦 Pelotas 大学）；CONICET III-LIDI；Comision de Investigaciones Cientificas Universidad Nacional de La Plata（科学委员会国立拉普拉塔大学）；Universidade Federal de Pelotas（联邦 Pelotas 大学）
