If two sentences have the same meaning, it should follow that they are
equivalent in their inferential properties, i.e., each sentence should
textually entail the other. However, many paraphrase datasets currently in
widespread use rely on a sense of paraphrase based on word overlap and syntax.
Can we teach them instead to identify paraphrases in a way that draws on the
inferential properties of the sentences, and is not over-reliant on lexical and
syntactic similarities of a sentence pair? We apply the adversarial paradigm to
this question, and introduce a new adversarial method of dataset creation for
paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks
participants to generate semantically equivalent (in the sense of mutually
implicative) but lexically and syntactically disparate paraphrases. These
sentence pairs can then be used both to test paraphrase identification models
(which get barely random accuracy) and then improve their performance. To
accelerate dataset generation, we explore automation of APT using T5, and show
that the resulting dataset also improves accuracy. We discuss implications for
paraphrase detection and release our dataset in the hope of making paraphrase
detection models better able to detect sentence-level meaning equivalence.

通过对抗方法提出了一个新的数据集创建方式 —— 对抗性同义句生成任务（Adversarial Paraphrasing Task， APT），以更好地检测句级别的意义相等，从而加速数据集生成并提高同义句识别模型的性能。