We introduce CGA, a conditional VAE architecture, to control, generate, and augment text. CGA is able to generate natural English sentences controlling multiple semantic and syntactic attributes by combining adversarial learning with a context-aware loss and a cyclical word dropout routine. We demonstrate the value of the individual model components in an ablation study. The scalability of our approach is ensured through a single discriminator, independently of the number of attributes. We show high quality, diversity and attribute control in the generated sentences through a series of automatic and human assessments. As the main application of our work, we test the potential of this new NLG model in a data augmentation scenario. In a downstream NLP task, the sentences generated by our CGA model show significant improvements over a strong baseline, and a classification performance often comparable to adding same amount of additional real data.
In this paper, we present SANTA, a scalable framework to automatically normalize E-commerce attribute values (e.g. "Win 10 Pro") to a fixed set of pre-defined canonical values (e.g. "Windows 10"). Earlier works on attribute normalization focused on fuzzy string matching (also referred as syntactic matching in this paper). In this work, we first perform an extensive study of nine syntactic matching algorithms and establish that 'cosine' similarity leads to best results, showing 2.7% improvement over commonly used Jaccard index. Next, we argue that string similarity alone is not sufficient for a…
KAUST（阿卜杜拉国王科技大学）；Ant Group（蚂蚁集团）；Xiaomi AI Lab（小米AI实验室）；University of Notre Dame（圣母大学）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）
As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples. However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between different cases. We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two attributes: (1) low-quality (the dialog model cannot generate a high-quality response f…
The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations. We argue that predicting the target word and attributes simultaneously is an effective way to ensure that translations are more faithful to the training data distribution with respect to these attributes. Experimental results on two tasks, uppercased input translation and gender prediction, show that this strategy helps mirror the training data distribution in testing. It also facilitates data augmentation on the task of uppercased input…
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）
Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching
Department of Artificial Intelligence and Informatics, Mayo Clinic（人工智能与信息学系，梅奥诊所）；Computer Science Department, University of Tulsa（图兰大学计算机科学系）；Mayo Clinic Comprehensive Cancer Center, Mayo Clinic（梅奥诊所综合癌症中心，梅奥诊所）；Division of Community Pediatric and Adolescent Medicine, Department of Pediatrics, Mayo Clinic（社区儿科与青少年医学分会，儿科部，梅奥诊所）；Department of Neurology, Mayo Clinic（神经病学部，梅奥诊所）；Department of Radiation Oncology, Mayo Clinic（放射肿瘤学部，梅奥诊所）；Department of Quantitative Health Sciences, Mayo Clinic（定量健康科学部，梅奥诊所）
