A Frame Tracking Model for Memory-Enhanced Dialogue Systems

Hannes Schulz, Jeremie Zumer, Layla El Asri, Shikhar Sharma


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.


    title     = {A Frame Tracking Model for Memory-Enhanced Dialogue Systems},
    author    = {Schulz, Hannes  and
                 Zumer, Jeremie  and
                 El Asri, Layla  and
                 Sharma, Shikhar},
    booktitle = {Proceedings of the 2nd Workshop on Representation Learning for {NLP}},
    month     = {August},
    year      = {2017},
    address   = {Vancouver, Canada},
    publisher = {Association for Computational Linguistics},
    url       = {https://www.aclweb.org/anthology/W17-2626},
    doi       = {10.18653/v1/W17-2626},
    pages     = {219--227}