| CPC G06F 40/295 (2020.01) | 21 Claims |
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1. A computer-implemented method, comprising:
identifying, by one or more processors of a computing system, a soft knowledge prompt in response to a received input text, wherein the received input text masks an object entity;
concatenating, by the one or more processors, the identified soft knowledge prompt to a sequence of word embeddings of the input text;
applying, by the one or more processors, the concatenated soft knowledge prompt and the sequence of word embeddings to a trained language model;
predicting, by the one or more processors, an object entity name of the object entity;
computing, by the one or more processors, a cross-entropy loss according to the predicted object entity name;
updating the identified soft knowledge prompt based on the computed cross-entropy loss, wherein the updated soft knowledge prompt is configured to function as a part of an auxiliary memory of a language model that is activated when solving a specific task; and
disambiguating a named entity that appears in the received input text.
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