The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive
semantic resource, consisting of a list of phrase pairs with (heuristic)
confidence estimates. However, it is still unclear how it can best be used, due
to the heuristic nature of the confidences and its necessarily incomplete
coverage. We propose models to leverage the phrase pairs from the PPDB to build
parametric paraphrase models that score paraphrase pairs more accurately than
the PPDB's internal scores while simultaneously improving its coverage. They
allow for learning phrase embeddings as well as improved word embeddings.
Moreover, we introduce two new, manually annotated datasets to evaluate
short-phrase paraphrasing models. Using our paraphrase model trained using
PPDB, we achieve state-of-the-art results on standard word and bigram
similarity tasks and beat strong baselines on our new short phrase paraphrase
tasks.

本文介绍了如何利用 Paraphrase Database 数据库中的短语对来构建参数化的释义模型，进而提高其覆盖范围和准确性，并介绍了两个人工标注的数据集以评估这些模型。使用我们的参数化释义模型，我们在标准单词和双字词相似度任务上实现了最佳结果，并在新的短语释义任务中击败了强大的基线模型。