  We introduce a new task of entailment relation aware paraphrase generation
which aims at generating a paraphrase conforming to a given entailment relation
(e.g. equivalent, forward entailing, or reverse entailing) with respect to a
given input. We propose a reinforcement learning-based weakly-supervised
paraphrasing system, ERAP, that can be trained using existing paraphrase and
natural language inference (NLI) corpora without an explicit task-specific
corpus. A combination of automated and human evaluations show that ERAP
generates paraphrases conforming to the specified entailment relation and are
of good quality as compared to the baselines and uncontrolled paraphrasing
systems. Using ERAP for augmenting training data for downstream textual
entailment task improves performance over an uncontrolled paraphrasing system,
and introduces fewer training artifacts, indicating the benefit of explicit
control during paraphrasing.
