This paper presents an effective method for generating natural language sentences from their underlying meaning representations. The method is built on top of a hybrid tree representation that jointly encodes both the meaning representation as well as the natural language in a tree structure. By using a tree conditional random field on top of the hybrid tree representation, we are able to explicitly model phrase-level dependencies amongst neighboring natural language phrases and meaning representation components in a simple and natural way. We show that the additional dependencies captured by the tree conditional random field allows it to perform better than directly inverting a previously developed hybrid tree semantic parser. Furthermore, we demonstrate that the model performs better than a previous state-of-the-art natural language generation model. Experiments are performed on two benchmark corpora with standard automatic evaluation metrics.
REF uses a conditional random field model over trees.
Natural Language Generation with Tree Conditional Random Fields
EMNLP
Computer Science