In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (NAACL/HLT-2007), pp. 172-179, Rochester, NY, April 2007.
Paper ID: 197
Category: Natural Language Learning, Learning for Semantic Parsing, Advice-Taking Learners
This paper explores the use of statistical machine translation (SMT) methods for tactical natural language generation. We present results on using phrase-based SMT for learning to map meaning representations to natural language. Improved results are obtained by inverting a semantic parser that uses SMT methods to map sentences into meaning representations. Finally, we show that hybridizing these two approaches results in still more accurate generation systems. Automatic and human evaluation of generated sentences are presented across two domains and four languages.
