Grammar-based natural language generation is lacking robustness in implementation because it is virtually incapable for learning. Statistical generation through language models is expensive due to overgeneration and its bias to short strings. Because dialogue utterances render intentions, learning model for the response generation systems should consider all utterances as equally good regardless of length or grammar. An intention-based architecture has been developed to generate response utterances in dialogue systems. This architecture is called classification-and-raking. In this architecture, response is deliberately chosen from dialogue corpus rather than wholly generated, such that it allows short ungrammatical utterances as long as they satisfy the intended meaning of input utterance. The proposed architecture is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The results from the comparative experiment show 91.2% recognition accuracy in classification-and-ranking as opposed to an average of 68.6% accuracy in overgeneration-and-ranking.

Intentions, Speech Acts, Dialogue System, Natural Language Generation, Classification-and-Ranking