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
- Editors:
- Phil Blunsom, Antoine Bordes, Kyunghyun Cho, Shay Cohen, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Yih
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 219–227
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/W17-2626/
- DOI:
- 10.18653/v1/W17-2626
- Cite (ACL):
- Hannes Schulz, Jeremie Zumer, Layla El Asri, and Shikhar Sharma. 2017. A Frame Tracking Model for Memory-Enhanced Dialogue Systems. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 219–227, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Frame Tracking Model for Memory-Enhanced Dialogue Systems (Schulz et al., RepL4NLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/W17-2626.pdf
- Data
- Frames Dataset