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.

本研究提出了一个新的语义合成共享方式，使用一个共享的元网络来捕捉语义合成的元知识并生成任务特定的语义合成模型的参数，实验表明这种方法在文本分类和序列标注等任务中具有许多优势，并且我们的提议的模型学习到的共享的元知识可以作为现成知识很容易地转移到新的任务中。