US 11,893,347 B2
Contrastive meta-learning for zero-shot learning
Tassilo Klein, Berlin (DE); and Moin Nabi, Berlin (DE)
Assigned to SAP SE, Walldorf (DE)
Filed by SAP SE, Walldorf (DE)
Filed on Jun. 1, 2021, as Appl. No. 17/335,823.
Prior Publication US 2022/0382979 A1, Dec. 1, 2022
Int. Cl. G06F 40/284 (2020.01); G06N 20/00 (2019.01); G06F 40/30 (2020.01)
CPC G06F 40/284 (2020.01) [G06F 40/30 (2020.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer implemented method for natural language processing, the method comprising:
receiving, by a tokenization module, a base sentence and one or more sentences comprising a semantic perturbation of the base sentence as an input, wherein the semantic perturbation of the base sentence comprises one or more linguistic deviations of the base sentence from a first version;
tokenizing, by the tokenization module, the input to generate a sequence of tokens;
embedding, by a machine learning engine, tokens of the semantic perturbation with tokens of the base sentence as tokens pairs to generate training data;
classifying, by a classifier, the semantic perturbation of the token pairs to capture relationships of the base sentence and the one or more sentences to generate a classification; and
training, by the machine learning engine, a language model based at least in part on the training data and the classification; and
wherein at least one of the receiving, tokenizing, determining, embedding and training are performed by one or more computers.