A Frame Tracking Model for Memory-Enhanced Dialogue Systems
This addresses the problem of handling complex, multi-goal dialogues for users, but it is incremental as it builds on existing tasks and datasets.
The paper tackles the frame tracking task in dialogue systems, which involves tracking multiple user goals, and shows that their model significantly outperforms a rule-based baseline on the Frames dataset.
Recently, resources and tasks were proposed to go beyond state tracking in dialogue systems. An example is the frame tracking task, which requires recording multiple frames, one for each user goal set during the dialogue. This allows a user, for instance, to compare items corresponding to different goals. This paper proposes a model which takes as input the list of frames created so far during the dialogue, the current user utterance as well as the dialogue acts, slot types, and slot values associated with this utterance. The model then outputs the frame being referenced by each triple of dialogue act, slot type, and slot value. We show that on the recently published Frames dataset, this model significantly outperforms a previously proposed rule-based baseline. In addition, we propose an extensive analysis of the frame tracking task by dividing it into sub-tasks and assessing their difficulty with respect to our model.