Jul 12, 2021 · In this demonstration, we present an efficient BERT-based multi-task (MT) framework that is particularly suitable for iterative and incremental development of ...
Abstract. We present an efficient BERT-based multi-task (MT) framework that is particularly suitable for iterative and incremental development of the tasks.
(a) For each task we train separately a task-specific model with partial fine-tuning, i.e. only the weights from some topmost layers (blue and red blocks) of ...
Jul 12, 2021 · In this demonstration, we present an efficient BERT-based multi-task (MT) framework that is particularly suitable for iterative and ...
We present an efficient BERT-based multi-task (MT) framework that is particularly suitable for iterative and incremental development of the tasks.
The partially fine-tuned merged model. 5. Page 6. provides the flexibility to update the model for frequently changing tasks without affecting other tasks, and ...
翻译：在这一演示中,我们提出了一个高效的基于BERT的多任务框架,特别适合任务的迭接和渐进发展。拟议框架基于部分微调的设想,即只微调BERT的一些顶层,同时保持其他层的冻结 ...
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Duration: 19:47
Posted: Sep 12, 2022
Posted: Sep 12, 2022
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The proposed framework allows flexible configuration, such as input data, text embedding extraction, mixture-of-experts, loss function, etc. In the future ...
A multi-task learning approach based on a shared BERT model to construct a multi-task learning network, which is trained by strongly and weakly related ...
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