Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
Table 2: Hyper-parameters used in our systems.
['[EMPTY]', 'Hyper-parameter', 'Value']
S3SS2SSS0Px1 The training procedure for multi-task sequence labeling models is as follows. For same-level-shared models, at each iteration, we first sample a task (main or auxiliary tasks) by Bernoulli trial based on sizes of datasets. Next, we sample a batch of training examples from the given task and then update gradients for both the shared parameters and the task-specific parameters according to the loss function of the given task. We use stochastic gradient descent algorithm with decay rate 0.05.