INSTANCE ADAPTIVE TRAINING WITH NOISE ROBUST LOSSES AGAINST NOISY LABELS

    公开(公告)号:US20230196087A1

    公开(公告)日:2023-06-22

    申请号:US17510782

    申请日:2021-10-26

    IPC分类号: G06N3/08 G06N3/04 G06K9/62

    摘要: There is included a method and apparatus comprising computer code for a joint training method using neural networks with noise-robust losses comprising encoding input tokens from a noisy dataset into input vectors using an input encoder; predicting a label based on the input vectors using a classifier model; calculating a beta value based on the input vectors and the label using a label quality predictor model, wherein the beta value is instance-specific for each training instance; and j oint training more than one model using a first modified loss function based on the beta value and an entropy value.

    BRIDGING SEMANTICS BETWEEN WORDS AND DEFINITIONS VIA ALIGNING WORD SENSE INVENTORIES

    公开(公告)号:US20230132090A1

    公开(公告)日:2023-04-27

    申请号:US17508417

    申请日:2021-10-22

    IPC分类号: G06F40/30 G06F40/284 G06N3/04

    摘要: There is included a method and apparatus comprising computer code configured to cause a processor or processors to perform generating one or more aligned inventories, wherein the one or more aligned inventories are generated using one or more word sense inventories, obtaining a word in a context sentence, determining one or more semantic equivalence scores indicating semantic similarity between the word in the context sentence and each of one or more associated glosses in the one or more aligned inventories using a semantic equivalence recognizer model, and predicting a correct sense of the word in the context sentence based on the determined one or more semantic equivalence scores.