Language Generation via DAG Transduction
Yajie Ye, Weiwei Sun, Xiaojun Wan · 2018
A DAG automaton is a formal device for manipulating graphs.By augmenting a DAG automaton with transduction rules, a DAG transducer has potential applications in fundamental NLP tasks.In this paper, we propose a novel DAG transducer to perform graph-to-program transformation.The target structure of our transducer is a program licensed by a declarative programming language rather than linguistic structures.By executing such a program, we can easily get a surface string.Our transducer is designed especially for natural language generation (NLG) from type-logical semantic graphs.Taking Elementary Dependency Structures, a format of English Resource Semantics, as input, our NLG system achieves a BLEU-4 score of 68.07.This remarkable result demonstrates the feasibility of applying a DAG transducer to resolve NLG, as well as the effectiveness of our design.