Abstract
by the inventors GUNASEKARA, Chulaka (New Hyde Park, NY, US); JOSHI, Sachindra (Gurgaon, IN); LEV, Guy (Tel Aviv, IL); SHNARCH, Eyal (Haifa, IL); SLONIM, Noam (Jerusalem, IL); SZNAJDER, Benjamin (Jerusalem, IL), filed on July 21, 2022, was made available online on January 25, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “The present techniques relate to training machine learning networks. More specifically, the techniques relate to training machine learning networks with unlabeled data.” In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “According to an embodiment described herein, a system can include processor to split unlabeled data into a plurality of groups corresponding to different perspectives. The processor can also further generate weakly labeled data for each of the plurality of groups using a respective associated heuristic. The processor can also inter-train a pretrained model for each perspective based on respective weakly labeled data. The processor can fine-tune each inter-trained model based on few-shot training data for each different perspective to generate a final model for each different perspective “According to another embodiment described herein, a method can include receiving, via a processor, unlabeled data, few-shot training data, and a pre-trained model. The method can further include splitting, via the processor, the unlabeled data into a plurality of groups corresponding to different perspectives. The method can also further include generating, via the processor, weakly labeled data for each of the plurality of groups using a respective associated heuristic. The method can also include inter-training, via the processor, the pre-trained model for each different perspective based on respective weakly labeled data. The processor can include fine-tuning, via the processor, each inter-trained model based on the few-shot training data for each different perspective to generate a final model for each different perspective.
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