Evaluating Human-Machine Conversation for Appropriateness

Abstract

Evaluation of complex, collaborative dialogue systems is a difficult task. Traditionally, developers have relied upon subjective feedback from the user, and parametrisation over observable metrics. However, both models place some reliance on the notion of a task; that is, the system is helping to user achieve some clearly defined goal, such as book a flight or complete a banking transaction. It is not clear that such metrics are as useful when dealing with a system that has a more complex task, or even no definable task at all, beyond maintain and performing a collaborative dialogue. Working within the EU funded COMPANIONS program, we investigate the use of appropriateness as a measure of conversation quality, the hypothesis being that good companions need to be good conversational partners . We report initial work in the direction of annotating dialogue for indicators of good conversation, including the annotation and comparison of the output of two generations of the same dialogue system.

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