公开(公告)号：US20230419164A1

公开(公告)日：2023-12-28

申请号：US17846428

申请日：2022-06-22

Applicant: Adobe Inc.

Inventor： Khalil Mrini ,  Franck Dernoncourt ,  Seunghyun Yoon ,  Trung Huu Bui ,  Walter W. Chang ,  Emilia Farcas ,  Ndapandula T. Nakashole

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Multitask machine-learning model training and training data augmentation techniques are described. In one example, training is performed for multiple tasks simultaneously as part of training a multitask machine-learning model using question pairs. Examples of the multiple tasks include question summarization and recognizing question entailment. Further, a loss function is described that incorporates a parameter sharing loss that is configured to adjust an amount that parameters are shared between corresponding layers trained for the first and second tasks, respectively. In an implementation, training data augmentation techniques are also employed by synthesizing question pairs, automatically and without user intervention, to improve accuracy in model training.