# An End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog

arXiv: https://arxiv.org/abs/1708.05956 · HTML: https://mlsanity.com/paper/1708.05956/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2017.

## Built on by (most cited first, 17 of 17)

- [ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://mlsanity.com/paper/2306.01386/) (2023)
- [Transferable Dialogue Systems and User Simulators](https://mlsanity.com/paper/2107.11904/) (2021)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems](https://mlsanity.com/paper/2109.04314/) (2021)
- [A Challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Systems](https://mlsanity.com/paper/2207.02657/) (2022)
- [NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback](https://mlsanity.com/paper/2110.02148/) (2021)
- [Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems](https://mlsanity.com/paper/2204.06452/) (2022)
- [Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset](https://mlsanity.com/paper/2209.13464/) (2022)
- [XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking](https://mlsanity.com/paper/2204.05895/) (2022)
- [Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2311.06345/) (2023)
- [Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking](https://mlsanity.com/paper/2311.10271/) (2023)
- [A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding](https://mlsanity.com/paper/2409.15861/) (2024)
- [Improved Goal Oriented Dialogue via Utterance Generation and Look Ahead](https://mlsanity.com/paper/2110.12412/) (2021)
- [Ontology-Enhanced Slot Filling](https://mlsanity.com/paper/2108.11275/) (2021)
- [Improving Dialogue State Tracking by Joint Slot Modeling](https://mlsanity.com/paper/2109.14144/) (2021)
- [Jointly Reinforced User Simulator and Task-oriented Dialog System with Simplified Generative Architecture](https://mlsanity.com/paper/2210.06706/) (2022)

## Abstract (arXiv)

We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and incorporate structured KB query results into system responses to successfully complete task-oriented dialogs. The proposed model produces well-structured system responses by jointly learning belief tracking and KB result processing conditioning on the dialog history. We evaluate the model in a restaurant search domain using a dataset that is converted from the second Dialog State Tracking Challenge (DSTC2) corpus. Experiment results show that the proposed model can robustly track dialog state given the dialog history. Moreover, our model demonstrates promising results in producing appropriate system responses, outperforming prior end-to-end trainable neural network models using per-response accuracy evaluation metrics.