  Many tasks in natural language processing can be viewed as multi-label
classification problems. However, most of the existing models are trained with
the standard cross-entropy loss function and use a fixed prediction policy
(e.g., a threshold of 0.5) for all the labels, which completely ignores the
complexity and dependencies among different labels. In this paper, we propose a
meta-learning method to capture these complex label dependencies. More
specifically, our method utilizes a meta-learner to jointly learn the training
policies and prediction policies for different labels. The training policies
are then used to train the classifier with the cross-entropy loss function, and
the prediction policies are further implemented for prediction. Experimental
results on fine-grained entity typing and text classification demonstrate that
our proposed method can obtain more accurate multi-label classification
results.
