三元组到文本：通过优化逆KL发散将RDF三元组转换为高质量自然语言
Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence
摘要：
知识库是信息结构化表示的主要形式之一。知识库通常由描述实体及其关系的资源描述框架（RDF）三元组组成。生成知识库的自然语言描述是自然语言处理中的一个重要任务，它被表述为一个条件语言生成任务，并使用序列到序列框架来处理。现有的训练语言模型的方法大多采用最大似然估计法，这往往会产生糟糕的句子。在本文中，我们认为，这样的问题的最大似然估计是内在的，这通常是不可撤销的，通过改变网络结构。因此，我们提出了一种新的三重文本（T2 T）框架，该框架近似优化了真实句子和生成句子的分布之间的逆Kullback-Leibler（KL）分歧。由于逆KL对虚假样本施加了很大的惩罚，所提出的方法可以显着降低生成低质量句子的概率。我们在三个真实世界数据集上的实验表明，T2 T可以生成更高质量的句子，并在几个评估指标上优于基线模型。
英文摘要：
Knowledge base is one of the main forms to represent information in a structured way. A knowledge base typically consists of Resource Description Frameworks (RDF) triples which describe the entities and their relations. Generating natural language description of the knowledge base is an important task in NLP, which has been formulated as a conditional language generation task and tackled using the sequence-to-sequence framework. Current works mostly train the language models by maximum likelihood estimation, which tends to generate lousy sentences. In this paper, we argue that such a problem of maximum likelihood estimation is intrinsic, which is generally irrevocable via changing network structures. Accordingly, we propose a novel Triple-to-Text (T2T) framework, which approximately optimizes the inverse Kullback-Leibler (KL) divergence between the distributions of the real and generated sentences. Due to the nature that inverse KL imposes large penalty on fake-looking samples, the proposed method can significantly reduce the probability of generating low-quality sentences. Our experiments on three real-world datasets demonstrate that T2T can generate higher-quality sentences and outperform baseline models in several evaluation metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
