  Structured sentences are important expressions in human writings and
dialogues. Previous works on neural text generation fused semantic and
structural information by encoding the entire sentence into a mixed hidden
representation. However, when a generated sentence becomes complicated, the
structure is difficult to be properly maintained. To alleviate this problem, we
explicitly separate the modeling process of semantic and structural
information. Intuitively, humans generate structured sentences by directly
connecting discourses with discourse markers (such as and, but, etc.).
Therefore, we propose a task that mimics this process, called discourse
transfer. This task represents a structured sentence as (head discourse,
discourse marker, tail discourse), and aims at tail discourse generation based
on head discourse and discourse marker. We also propose a corresponding model
called TransSent, which interprets the relationship between two discourses as a
translation1 from the head discourse to the tail discourse in the embedding
space. We experiment TransSent not only in discourse transfer task but also in
free text generation and dialogue generation tasks. Automatic and human
evaluation results show that TransSent can generate structured sentences with
high quality, and has certain scalability in different tasks.
