The Directed Acyclic Transformer is a fast non-autoregressive (NAR) model that performs well in Neural Machine Translation. Two issues prevent its application to general Natural Language Generation (NLG) tasks: frequent Out-Of-Vocabulary (OOV) errors and the inability to faithfully generate entity names. We introduce Control-DAG, a constrained decoding algorithm for our Directed Acyclic T5 (DA-T5) model which offers lexical, vocabulary and length control. We show that Control-DAG significantly enhances DA-T5 on the Schema Guided Dialogue and the DART datasets, establishing strong NAR results for Task-Oriented Dialogue and Data-to-Text NLG.

翻译：有向无环Transformer是一种快速的非自回归（NAR）模型，在神经机器翻译任务中表现优异。然而，频繁出现的词表外（OOV）错误以及无法忠实生成实体名称这两大问题，限制了其在通用自然语言生成（NLG）任务中的应用。我们提出Control-DAG——一种面向有向无环T5（DA-T5）模型的约束解码算法，该算法提供词汇级、词表级和长度级控制能力。实验表明，Control-DAG在Schema Guided Dialogue和DART数据集上显著提升了DA-T5的性能，为面向任务的对话系统以及数据到文本的NLG任务确立了强大的非自回归基线结果。