Modeling Task-Oriented Dialogue

A common tool for improving the performance quality of natural language processing systems is the use of contextual information for disambiguation. Here I describe the use of a finite state machine (FSM) to disambiguate speech acts in a machine translation system. The FSM has two layers that model, respectively, the global and local structures found in naturally-occurring conversations. The FSM has been modeled on a corpus of task-oriented dialogues in a travel planning situation. In the dialogues, one of the interactants is a travel agent or hotel clerk, and the other a client requesting information or services. A discourse processor based on the FSM was implemented in order to process contextual information in a machine translation system. Evaluation results show that the discourse processor is able to disambiguate and improve the quality of the dialogue translation. Other applications include human-computer interaction and computer-assisted language learning.