# A Frame Tracking Model for Memory-Enhanced Dialogue Systems

arXiv: https://arxiv.org/abs/1706.01690 · HTML: https://mlsanity.com/paper/1706.01690/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues](https://mlsanity.com/paper/2111.02574/) (2021)
- [DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2308.00878/) (2023)
- [QuOTE: Question-Oriented Text Embeddings](https://mlsanity.com/paper/2502.10976/) (2025)
- [EMMM, Explain Me My Model! Explainable Machine Generated Text Detection in Dialogues](https://mlsanity.com/paper/2508.18715/) (2025)

## Abstract (arXiv)

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.