Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead
This addresses dialogue failure in customer-care interactions, presenting an incremental improvement over existing methods.
The paper tackles the problem of customer intent misunderstanding in goal-oriented dialogue systems by training a deep text-to-text model to generate successive user utterances from unlabeled data, achieving improved intent prediction through a multi-task training regime and a novel look-ahead approach.
Goal oriented dialogue systems have become a prominent customer-care interaction channel for most businesses. However, not all interactions are smooth, and customer intent misunderstanding is a major cause of dialogue failure. We show that intent prediction can be improved by training a deep text-to-text neural model to generate successive user utterances from unlabeled dialogue data. For that, we define a multi-task training regime that utilizes successive user-utterance generation to improve the intent prediction. Our approach achieves the reported improvement due to two complementary factors: First, it uses a large amount of unlabeled dialogue data for an auxiliary generation task. Second, it uses the generated user utterance as an additional signal for the intent prediction model. Lastly, we present a novel look-ahead approach that uses user utterance generation to improve intent prediction in inference time. Specifically, we generate counterfactual successive user utterances for conversations with ambiguous predicted intents, and disambiguate the prediction by reassessing the concatenated sequence of available and generated utterances.