  Semantic composition functions have been playing a pivotal role in neural
representation learning of text sequences. In spite of their success, most
existing models suffer from the underfitting problem: they use the same shared
compositional function on all the positions in the sequence, thereby lacking
expressive power due to incapacity to capture the richness of compositionality.
Besides, the composition functions of different tasks are independent and
learned from scratch. In this paper, we propose a new sharing scheme of
composition function across multiple tasks. Specifically, we use a shared
meta-network to capture the meta-knowledge of semantic composition and generate
the parameters of the task-specific semantic composition models. We conduct
extensive experiments on two types of tasks, text classification and sequence
tagging, which demonstrate the benefits of our approach. Besides, we show that
the shared meta-knowledge learned by our proposed model can be regarded as
off-the-shelf knowledge and easily transferred to new tasks.

Artificial Intelligence;Computation and Language