Yes/No or polar questions represent one of the main linguistic question categories. They consist of a main interrogative clause, for which the answer is binary (assertion or negation). Polar questions and answers (PQA) represent a valuable knowledge resource present in many community and other curated QA sources, such as forums or e-commerce applications. Using answers to polar questions alone in other contexts is not trivial. Answers are contextualized, and presume that the interrogative question clause and any shared knowledge between the asker and answerer are provided. We address the problem of controllable rewriting of answers to polar questions into decontextualized and succinct factual statements. We propose a Transformer sequence to sequence model that utilizes soft-constraints to ensure controllable rewriting, such that the output statement is semantically equivalent to its PQA input. Evaluation on three separate PQA datasets as measured through automated and human evaluation metrics show that our proposed approach achieves the best performance when compared to existing baselines.

利用Transformer序列到序列模型，我们提出了可控重写极性问题的答案，形成无情境且简洁的事实陈述，实现与PQA输入的语义等价。通过自动化和人工评估指标，我们在三个不同的PQA数据集上评估，结果表明我们的方法在性能上优于现有基线模型。

可控的将是非问题和答案去除语境转化为事实陈述