Many tasks, including language generation, benefit from learning the
structure of the output space, particularly when the space of output labels is
large and the data is sparse. State-of-the-art neural language models
indirectly capture the output space structure in their classifier weights since
they lack parameter sharing across output labels. Learning shared output label
mappings helps, but existing methods have limited expressivity and are prone to
overfitting. In this paper, we investigate the usefulness of more powerful
shared mappings for output labels, and propose a deep residual output mapping
with dropout between layers to better capture the structure of the output space
and avoid overfitting. Evaluations on three language generation tasks show that
our output label mapping can match or improve state-of-the-art recurrent and
self-attention architectures, and suggest that the classifier does not
necessarily need to be high-rank to better model natural language if it is
better at capturing the structure of the output space.

本研究提出了一种深度残差输出映射方法，通过层与层之间的随机失活，更好地捕捉输出空间的结构，避免过拟合，经实验证明有助于提高自然语言生成任务的性能。