Single-task models (STMs) struggle to learn sophisticated representations from a finite set of annotated data. Multitask learning approaches overcome these constraints by simultaneously training various associated tasks, thereby learning generic representations among various tasks by sharing some layers of the neural network architecture. Because of this, multitask models (MTMs) have better generalization properties than those of single-task learning. Multitask model generalizations can be used to improve the results of other models. STMs can learn more sophisticated representations in the training phase by utilizing the extracted knowledge of an MTM through the knowledge distillation technique where one model supervises another model during training by using its learned generalizations. This paper proposes a knowledge distillation technique in which different MTMs are used as the teacher model to supervise different student models. Knowledge distillation is applied with different representations of the teacher model. We also investigated the effect of the conditional random field (CRF) and softmax function for the token-level knowledge distillation approach, and found that the softmax function leveraged the performance of the student model compared to CRF. The result analysis was also extended with statistical analysis by using the Friedman test.
Distilling Knowledge with a Teacher’s Multitask Model for Biomedical Named Entity Recognition †
Mehmood T.;Gerevini A. E.;Olivato M.;Serina I.
2023-01-01
Abstract
Single-task models (STMs) struggle to learn sophisticated representations from a finite set of annotated data. Multitask learning approaches overcome these constraints by simultaneously training various associated tasks, thereby learning generic representations among various tasks by sharing some layers of the neural network architecture. Because of this, multitask models (MTMs) have better generalization properties than those of single-task learning. Multitask model generalizations can be used to improve the results of other models. STMs can learn more sophisticated representations in the training phase by utilizing the extracted knowledge of an MTM through the knowledge distillation technique where one model supervises another model during training by using its learned generalizations. This paper proposes a knowledge distillation technique in which different MTMs are used as the teacher model to supervise different student models. Knowledge distillation is applied with different representations of the teacher model. We also investigated the effect of the conditional random field (CRF) and softmax function for the token-level knowledge distillation approach, and found that the softmax function leveraged the performance of the student model compared to CRF. The result analysis was also extended with statistical analysis by using the Friedman test.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
