Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review

Deep Neural Networks (DNNs) have led to unprecedented progress in various natural language processing (NLP) tasks. Owing to limited data and computation resources, using third-party data and models has become a new paradigm for adapting various tasks. However, research shows that it has some potential security vulnerabilities because attackers can manipulate the training process and data source. Such a way can set specific triggers, making the model exhibit expected behaviors that have little inferior influence on the model's performance for primitive tasks, called backdoor attacks. Hence, it could have dire consequences, especially considering that the backdoor attack surfaces are broad. To get a precise grasp and understanding of this problem, a systematic and comprehensive review is required to confront various security challenges from different phases and attack purposes. Additionally, there is a dearth of analysis and comparison of the various emerging backdoor countermeasures in this situation. In this paper, we conduct a timely review of backdoor attacks and countermeasures to sound the red alarm for the NLP security community. According to the affected stage of the machine learning pipeline, the attack surfaces are recognized to be wide and then formalized into three categorizations: attacking pre-trained model with fine-tuning (APMF) or prompt-tuning (APMP), and attacking final model with training (AFMT), where AFMT can be subdivided into different attack aims. Thus, attacks under each categorization are combed. The countermeasures are categorized into two general classes: sample inspection and model inspection. Overall, the research on the defense side is far behind the attack side, and there is no single defense that can prevent all types of backdoor attacks. An attacker can intelligently bypass existing defenses with a more invisible attack. ......Comment: 24 pages, 4 figure