Response Generation in Dialogue Using a Tailored PCFG Parser
Caixia Yuan, Xiaojie Wang, Qianhui He · 2015
This paper presents a parsing paradigm for natural language generation task, which learns a tailored probabilistic context-free grammar for encoding meaning representation (MR) and its corresponding natural language (NL) expression, then decodes and yields natural language sentences at the leaves of the optimal parsing tree for a target meaning representation.The major advantage of our method is that it does not require any prior knowledge of the M-R syntax for training.We deployed our method in response generation for a Chinese spoken dialogue system, obtaining results comparable to a strong baseline both in terms of BLEU scores and human evaluation.