Papers with mask-then-infill procedure

2 papers
Contextualized Perturbation for Textual Adversarial Attack (2021.naacl-main)

Copied to clipboard

Challenge: Existing techniques for generating adversarial examples are driven by local heuristic rules that are agnostic to the context, resulting in unnatural and ungrammatical outputs.
Approach: They propose a ContextuaLized AdversaRial Example generation model that generates fluent and grammatical outputs through a mask-then-infill procedure.
Outcome: The proposed model outperforms baseline models in terms of attack success rate, textual similarity, fluency and grammaticality.
Controllable Fake Document Infilling for Cyber Deception (2022.findings-emnlp)

Copied to clipboard

Challenge: Existing approaches to deter malicious intrusion generate multiple fake versions of a document that are context-agnostic and produce sub-optimal outputs.
Approach: They propose a context-aware model for creating fake documents that masks important concepts of varied lengths and then infills a realistic but fake alternative considering both the previous and future contexts.
Outcome: The proposed model outperforms baseline models in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations