  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.
