# Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation

arXiv: https://arxiv.org/abs/1806.04441 · HTML: https://mlsanity.com/paper/1806.04441/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 16 of 16)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Conditional Generation with a Question-Answering Blueprint](https://mlsanity.com/paper/2207.00397/) (2022)
- [Effective Sequence-to-Sequence Dialogue State Tracking](https://mlsanity.com/paper/2108.13990/) (2021)
- [DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation](https://mlsanity.com/paper/2204.09149/) (2022)
- [Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System](https://mlsanity.com/paper/2109.11292/) (2021)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs](https://mlsanity.com/paper/2109.07396/) (2021)
- [Q-TOD: A Query-driven Task-oriented Dialogue System](https://mlsanity.com/paper/2210.07564/) (2022)
- [Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation](https://mlsanity.com/paper/2305.00034/) (2023)
- [Autoregressive Entity Generation for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2209.08708/) (2022)
- [Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2305.10149/) (2023)
- [Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems](https://mlsanity.com/paper/2405.15585/) (2024)
- [What Did You Say? Task-Oriented Dialog Datasets Are Not Conversational!?](https://mlsanity.com/paper/2203.03431/) (2022)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation](https://mlsanity.com/paper/2406.09881/) (2024)
- [IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations](https://mlsanity.com/paper/2609.04030/) (2026)

## Abstract (arXiv)

Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset shows that our framework significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.