no code implementations • 18 Jul 2022 • MingBin Xu, Congzheng Song, Ye Tian, Neha Agrawal, Filip Granqvist, Rogier Van Dalen, Xiao Zhang, Arturo Argueta, Shiyi Han, Yaqiao Deng, Leo Liu, Anmol Walia, Alex Jin
Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP.
Operations using sparse structures are common in natural language models at the input and output layers, because these models operate on sequences over discrete alphabets.
Weighted finite-state transducers (FSTs) are frequently used in language processing to handle tasks such as part-of-speech tagging and speech recognition.
Weighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such as morphological analysis, part-of-speech tagging, chunking, named entity recognition, speech recognition, and others.