A Frame Tracking Model for Memory-Enhanced Dialogue Systems

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


Abstract
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.
Anthology ID:
W17-2626
Volume:
Proceedings of the 2nd Workshop on Representation Learning for NLP
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
RepL4NLP | WS
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
219–227
Language:
URL:
https://www.aclweb.org/anthology/W17-2626
DOI:
10.18653/v1/W17-2626
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2626.pdf