# TOD-Flow: Modeling the Structure of Task-Oriented Dialogues

arXiv: https://arxiv.org/abs/2312.04668 · HTML: https://mlsanity.com/paper/2312.04668/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 24 of 24)

- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Modular Multitask Reinforcement Learning with Policy Sketches](https://mlsanity.com/paper/1611.01796/) (2016)
- [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://mlsanity.com/paper/2004.06871/) (2020)
- [GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection](https://mlsanity.com/paper/2111.14592/) (2021)
- [Recent Advances and Challenges in Task-oriented Dialog System](https://mlsanity.com/paper/2003.07490/) (2020)
- [Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration](https://mlsanity.com/paper/1807.03480/) (2018)
- [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://mlsanity.com/paper/2106.05555/) (2021)
- [Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention](https://mlsanity.com/paper/1905.12866/) (2019)
- [Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies](https://mlsanity.com/paper/1807.07665/) (2018)
- [SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation](https://mlsanity.com/paper/2209.06664/) (2022)
- [BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1711.05715/) (2017)
- [A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning](https://mlsanity.com/paper/2202.13675/) (2022)
- [GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2010.01447/) (2020)
- [Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System](https://mlsanity.com/paper/2006.06814/) (2020)
- [Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies](https://mlsanity.com/paper/2001.00248/) (2020)
- [End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs](https://mlsanity.com/paper/2109.07263/) (2021)
- [Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations](https://mlsanity.com/paper/2305.14556/) (2023)
- [Multimodal Subtask Graph Generation from Instructional Videos](https://mlsanity.com/paper/2302.08672/) (2023)
- [Learning Dialog Policies from Weak Demonstrations](https://mlsanity.com/paper/2004.11054/) (2020)
- [Unsupervised Task Graph Generation from Instructional Video Transcripts](https://mlsanity.com/paper/2302.09173/) (2023)
- [Learning Parameterized Task Structure for Generalization to Unseen Entities](https://mlsanity.com/paper/2203.15034/) (2022)
- [Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization](https://mlsanity.com/paper/2205.12648/) (2022)
- [GKS: Graph-based Knowledge Selector for Task-oriented Dialog System](https://mlsanity.com/paper/2112.03719/) (2021)

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

- [Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction](https://mlsanity.com/paper/2410.18481/) (2024)
- [Unsupervised Mutual Learning of Discourse Parsing and Topic Segmentation in Dialogue](https://mlsanity.com/paper/2405.19799/) (2024)
- [Transition-Matrix Regularization for Next Dialogue Act Prediction in Counselling Conversations](https://mlsanity.com/paper/2604.18539/) (2026)
- [Training Zero-Shot Generalizable End-to-End Task-Oriented Dialog System Without Turn-level Dialog Annotations](https://mlsanity.com/paper/2407.15055/) (2024)

## Abstract (arXiv)

Task-Oriented Dialogue (TOD) systems have become crucial components in interactive artificial intelligence applications. While recent advances have capitalized on pre-trained language models (PLMs), they exhibit limitations regarding transparency and controllability. To address these challenges, we propose a novel approach focusing on inferring the TOD-Flow graph from dialogue data annotated with dialog acts, uncovering the underlying task structure in the form of a graph. The inferred TOD-Flow graph can be easily integrated with any dialogue model to improve its prediction performance, transparency, and controllability. Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction. We show that the proposed TOD-Flow graph better resembles human-annotated graphs compared to prior approaches. Furthermore, when combined with several dialogue policies and end-to-end dialogue models, we demonstrate that our approach significantly improves dialog act classification and end-to-end response generation performance in the MultiWOZ and SGD benchmarks. Code available at: https://github.com/srsohn/TOD-Flow