Hybrid Natural Language Generation For Spoken Dialogue Systems

Proceedings of the 7th European Conference on Speech Communication and Technology (EUROSPEECH–01) |

Publication

The natural language generation component of most dialogue systems is based on templates. Template-based generators are hard to maintain and reuse, and the sentences they produce lack the variability and robustness needed by conversational systems. In this paper, a flexible and domain-independent natural language generator for spoken dialogue systems is proposed which combines fixed surface expressions with freely generated text. The generation algorithm follows a hybrid approach, combining finite state machine (FSM) grammars and corpus-based language models. In this approach, the FSM grammar (a reversible parser grammar) is constrained by a word and concept n-gram that takes terminals and non-terminal co-occurrences into account. The n-gram grammar helps prevent inappropriate derivations, therefore improving the quality of the generated texts. The proposed algorithm achieves faster than real-time performance because of the limited number of derivations.