Knowledge-enhanced text generation aims to enhance the quality of generated text by utilizing internal or external knowledge sources. While language models have demonstrated impressive capabilities in generating coherent and fluent text, the lack of interpretability presents a substantial obstacle. The limited interpretability of generated text significantly impacts its practical usability, particularly in knowledge-enhanced text generation tasks that necessitate reliability and explainability. Existing methods often employ domain-specific knowledge retrievers that are tailored to specific data characteristics, limiting their generalizability to diverse data types and tasks. To overcome this limitation, we directly leverage the two-tier architecture of structured knowledge, consisting of high-level entities and low-level knowledge triples, to design our task-agnostic structured knowledge hunter. Specifically, we employ a local-global interaction scheme for structured knowledge representation learning and a hierarchical transformer-based pointer network as the backbone for selecting relevant knowledge triples and entities. By combining the strong generative ability of language models with the high faithfulness of the knowledge hunter, our model achieves high interpretability, enabling users to comprehend the model output generation process. Furthermore, we empirically demonstrate the effectiveness of our model in both internal knowledge-enhanced table-to-text generation on the RotoWireFG dataset and external knowledge-enhanced dialogue response generation on the KdConv dataset. Our task-agnostic model outperforms state-of-the-art methods and corresponding language models, setting new standards on the benchmark.

翻译：知识增强文本生成旨在通过利用内部或外部知识源提升生成文本的质量。尽管语言模型在生成连贯流畅的文本方面展现出卓越能力，但其可解释性的缺失构成了显著障碍。生成文本有限的可解释性严重影响了其实际可用性，尤其在需要可靠性与可解释性的知识增强文本生成任务中。现有方法通常采用针对特定数据特征设计的领域知识检索器，限制了其在不同数据类型与任务中的泛化能力。为克服此局限，我们直接利用由高层实体与底层知识三元组构成的结构化知识双层架构，设计了任务无关的结构化知识猎手。具体而言，我们采用局部-全局交互机制进行结构化知识表示学习，并以基于分层Transformer的指针网络作为核心架构，用于选择相关知识三元组与实体。通过结合语言模型的强大生成能力与知识猎手的高可信度，我们的模型实现了高可解释性，使用户能够理解模型输出生成过程。此外，我们通过实验验证了模型在RotoWireFG数据集上的内部知识增强表格到文本生成任务，以及在KdConv数据集上的外部知识增强对话响应生成任务中的有效性。我们的任务无关模型超越了现有最优方法及相应语言模型，在基准测试中树立了新标准。