A Quality-based Syntactic Template Retriever for
  Syntactically-controlled Paraphrase Generation
Xue Zhang Songming Zhang Yunlong Liang Yufeng Chen Jian Liu Wenjuan Han Jinan Xu
Existing syntactically-controlled paraphrase generation (SPG) models perform
promisingly with human-annotated or well-chosen syntactic templates. However,
the difficulty of obtaining such templates actually hinders the practical
application of SPG models. For one thing, the prohibitive cost makes it
unfeasible to manually design decent templates for every source sentence. For
another, the templates automatically retrieved by current heuristic methods are
usually unreliable for SPG models to generate qualified paraphrases. To escape
this dilemma, we propose a novel Quality-based Syntactic Template Retriever
(QSTR) to retrieve templates based on the quality of the to-be-generated
paraphrases. Furthermore, for situations requiring multiple paraphrases for
each source sentence, we design a Diverse Templates Search (DTS) algorithm,
which can enhance the diversity between paraphrases without sacrificing
quality. Experiments demonstrate that QSTR can significantly surpass existing
retrieval methods in generating high-quality paraphrases and even perform
comparably with human-annotated templates in terms of reference-free metrics.
Additionally, human evaluation and the performance on downstream tasks using
our generated paraphrases for data augmentation showcase the potential of our
QSTR and DTS algorithm in practical scenarios.