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.

这篇文章介绍了一种新的基于强化学习的弱监督的改述系统 ERAP，通过使用现有的改述和自然语言推理 (NLI) 语料库进行训练，可以生成符合给定蕴涵关系且质量良好的改述，经实验证明，使用 ERAP 来增强下游的文本蕴涵任务训练数据可以提高性能并减少训练异常，表明了在改述过程中显式控制的好处。

蕴含关系感知的释义生成

Entailment Relation Aware Paraphrase Generation

Universal schema (USchema) assumes that two sentence patterns that share the
same entity pairs are similar to each other. This assumption is widely adopted
for solving various types of relation extraction (RE) tasks. Nevertheless, each
sentence pattern could contain multiple facets, and not every facet is similar
to all the facets of another sentence pattern co-occurring with the same entity
pair. To address the violation of the USchema assumption, we propose
multi-facet universal schema that uses a neural model to represent each
sentence pattern as multiple facet embeddings and encourage one of these facet
embeddings to be close to that of another sentence pattern if they co-occur
with the same entity pair. In our experiments, we demonstrate that multi-facet
embeddings significantly outperform their single-facet embedding counterpart,
compositional universal schema (CUSchema) (Verga et al., 2016), in distantly
supervised relation extraction tasks. Moreover, we can also use multiple
embeddings to detect the entailment relation between two sentence patterns when
no manual label is available.

提出一种使用神经模型将每个句子模式表示为多重面向嵌入，并鼓励其中一个嵌入与另一个具有相同实体对的句子模式的嵌入接近的多重面向通用模式，用于处理多类型的关系抽取任务，并且可以在没有手动标签的情况下，使用多元嵌入检测两个句子模式之间的蕴含关系。