Hybrid natural language generation for spoken dialogue systems

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, we propose a flexible and domainindependent natural language generator for spoken dialogue systems 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 -gram that takes terminals and non-terminal cooccurrences into account. The -gram grammar helps prevent inappropriate derivations, therefore improving the quality of the generated texts. Furthermore, the proposed algorithm achieves faster than real-time performance because of the limited number of derivations.