A Dialogue Analysis Model with Statistical Speech Act Processing for Dialogue Machine Translation
Jaewon Lee, Gil Chang Kim · 1997
In some cases, to make a proper trans- lation of an utterance in a dialogue, the system needs various information about context. In this paper, we propose a sta- tistical dialogue analysis model based on speech acts for Korean-English dialogue machine translation. The model uses syn- tactic patterns and N-grams reflecting the hierarchical discourse structures of dia- logues. The syntactic pattern includes the syntactic features that are related with the language dependent expressions of speech acts. The N-gram of speech acts based on hierarchical recency approximates the context. Our experimental results with trigram showed that the proposed model achieved 78.59 % accuracy for the top candidate and 99.06 % for the top four candi- dates even though the size of the training corpus is relatively small. The proposed model can be integrated with other approaches for an efficient and robust anal- ysis of dialogues.