Neural sentence simplification method based on sequence-to-sequence framework
has become the mainstream method for sentence simplification (SS) task.
Unfortunately, these methods are currently limited by the scarcity of parallel
SS corpus. In this paper, we focus on how to reduce the dependence on parallel
corpus by leveraging a careful initialization for neural SS methods from
paraphrase corpus. Our work is motivated by the following two findings: (1)
Paraphrase corpus includes a large proportion of sentence pairs belonging to SS
corpus. (2) We can construct large-scale pseudo parallel SS data by keeping
these sentence pairs with a higher complexity difference. Therefore, we propose
two strategies to initialize neural SS methods using paraphrase corpus. We
train three different neural SS methods with our initialization, which can
obtain substantial improvements on the available WikiLarge data compared with
themselves without initialization.

本文提出了两种使用释义语料库来初始化神经简句化模型的策略，通过参考释义语料库中复杂度较高的句子对可以构造大规模虚拟平行简句化数据，并将该方法用于训练三种不同的神经简句化模型，在 WikiLarge 数据集中相对于未初始化情况下获得了显著的提升。