Multiple User Intent Understanding for Spoken Dialog System

MasterOne of the main components of spoken language understanding is user intent detection. Common classification approaches to this problem cannot detect multiple user intents. In this thesis, a user intent indicator (UII), which is a phrase that represents user intent in an utterance, is introduced. Instead of tackling user intent detection directly, this approach predicts user intent by detecting UIIs and extracting their types. UII detection is formulated as a sequential labeling problem, and a linear-chain conditional random field is used for the sequential labeling model. Based on the advantages and disadvantages of the traditional and proposed models, a second-level predictor is introduced to capitalize on the strengths and compensate for the weaknesses of each model. A set of experiments showed that the UII detection model outperformed the baselines in F1-score when tested not only on utterances with multiple user intents but also on utterances with a single user intent. With the second-level predictor, the proposed method outperformed the baselines in all evaluation metrics. To demonstrate the effectiveness of this approach in reality, human experiments on an integrated dialog system were also performed, and the proposed method showed a shorter average turn length, a higher successful turn rate and a higher task completion rate than the baseline system