Spoken dialogue systems promise efficient and natural access to a large variety of information sources and services from any phone. However, current spoken dialogue systems are deficient in their strategies for preventing, identifying and repairing problems that arise in the conversation. This paper reports results on automatically training a Problematic Dialogue Predictor to predict problematic human-computer dialogues using a corpus of 4692 dialogues collected with the 'How May I Help You' (SM) spoken dialogue system. The Problematic Dialogue Predictor can be immediately applied to the system's decision of whether to transfer the call to a human customer care agent, or be used as a cue to the system's dialogue manager to modify its behavior to repair problems, and even perhaps, to prevent them. We show that a Problematic Dialogue Predictor using automatically-obtainable features from the first two exchanges in the dialogue can predict problematic dialogues 13.2% more accurately than the baseline.
BRIGHAM YOUNG UNIVERSITY As chair of the candidate's graduate committee, I have read the thesis of Robert Van Dam in its final form and have found that (1) its format, citations, and bibliographical style are consistent and acceptable and fulfill university and department style requirements; (2) its illustrative materials including figures, tables, and charts are in place; and (3) the final manuscript is satisfactory to the graduate committee and is ready for submission to the university library.
The parsing community has long recognized the importance of lexicalized models of syntax. By contrast, these models do not appear to have had an impact on the statistical NLG community. To prove their importance in NLG, we show that a lexicalized model of syntax improves the performance of a statistical text compression system, and show results that suggest it would also improve the performances of an MT application and a pure natural language generation system.
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