  Knowledge Graph (KG)-to-Text generation aims at generating fluent
natural-language text that accurately represents the information of a given
knowledge graph. While significant progress has been made in this task by
exploiting the power of pre-trained language models (PLMs) with appropriate
graph structure-aware modules, existing models still fall short of generating
faithful text, especially when the ground-truth natural-language text contains
additional information that is not present in the graph. In this paper, we
develop a KG-to-text generation model that can generate faithful
natural-language text from a given graph, in the presence of noisy reference
text. Our framework incorporates two core ideas: Firstly, we utilize
contrastive learning to enhance the model's ability to differentiate between
faithful and hallucinated information in the text, thereby encouraging the
decoder to generate text that aligns with the input graph. Secondly, we empower
the decoder to control the level of hallucination in the generated text by
employing a controllable text generation technique. We evaluate our model's
performance through the standard quantitative metrics as well as a
ChatGPT-based quantitative and qualitative analysis. Our evaluation
demonstrates the superior performance of our model over state-of-the-art
KG-to-text models on faithfulness.
