Stochastic language generation using situated PCFGs

Abstract

This paper presents a purely data-driven approach for generating natural language (NL) expressions from its corresponding semantic representations. Our aim is to exploit a parsing paradigm for natural language generation (NLG) task, which first encodes semantic representations with a situated probabilistic context-free grammar (PCFG), then decodes and yields natural sentences at the leaves of the optimal parsing tree. We deployed our system in two different domains, one is response generation for a Chinese spoken dialogue system, and the other is instruction generation for a virtual environment in English language, obtaining results comparable to state-of-the-art systems both in terms of BLEU scores and human evaluation.