Relying on the idea that back-of-the-book indexes are traditional devices for navigation through large documents, we have developed a method to build a hypertextual network that helps the navigation in a document. Building such an hypertextual network requires selecting a list of descriptors, identifying the relevant text segments to associate with each descriptor and finally ranking the descriptors and reference segments by relevance order. We propose a specific document segmentation method and a relevance measure for information ranking. The algorithms are tested on 4 corpora (of different types and domains) without human intervention or any semantic knowledge.
Uncovering and Aligning Anomalous Attention Heads to Defend Against NLP Backdoor Attacks
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；State Key Laboratory of Cyberspace Security Defense（网络空间安全防御国家重点实验室）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
Zhejiang University（浙江大学）；Nanyang Technological University（南洋理工大学）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Chongqing University（重庆大学）；Peng Cheng Laboratory（鹏城实验室）；Peking University（北京大学）
The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus
Facebook AI Research（脸书人工智能研究院）；University College London（伦敦大学学院）；University of Mannheim（曼海姆大学）；ENS, PSL University（巴黎高等师范学院，巴黎文理研究大学）；Inria（法国国家信息与自动化研究所）；University of Washington（华盛顿大学）
