# UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues

arXiv: https://arxiv.org/abs/2004.14307 · HTML: https://mlsanity.com/paper/2004.14307/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Rethinking Explainability as a Dialogue: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01875/) (2022)
- [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://mlsanity.com/paper/2106.05555/) (2021)
- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Domain State Tracking for a Simplified Dialogue System](https://mlsanity.com/paper/2103.06648/) (2021)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Task-Oriented Dialogue System as Natural Language Generation](https://mlsanity.com/paper/2108.13679/) (2021)
- [CHATEDIT: Towards Multi-turn Interactive Facial Image Editing via Dialogue](https://mlsanity.com/paper/2303.11108/) (2023)
- [HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management](https://mlsanity.com/paper/2105.06041/) (2021)
- [Multimodal Dialogue State Tracking](https://mlsanity.com/paper/2206.07898/) (2022)
- [Understanding and Evaluating Hallucinations in 3D Visual Language Models](https://mlsanity.com/paper/2502.15888/) (2025)
- [End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management](https://mlsanity.com/paper/2106.11796/) (2021)
- [Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users](https://mlsanity.com/paper/2310.20479/) (2023)
- [Taming Continuous Posteriors for Latent Variational Dialogue Policies](https://mlsanity.com/paper/2205.07633/) (2022)
- [UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs](https://mlsanity.com/paper/2209.07239/) (2022)
- [Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization](https://mlsanity.com/paper/2305.03987/) (2023)
- [Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking](https://mlsanity.com/paper/2312.01842/) (2023)
- [DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment](https://mlsanity.com/paper/2310.16319/) (2023)
- [Collaborative and Proactive Management of Task-Oriented Conversations](https://mlsanity.com/paper/2510.05110/) (2025)

## Abstract (arXiv)

Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons. First, tracking dialogue states of multiple domains is non-trivial as the dialogue agent must obtain complete states from all relevant domains, some of which might have shared slots among domains as well as unique slots specifically for one domain only. Second, the dialogue agent must also process various types of information across domains, including dialogue context, dialogue states, and database, to generate natural responses to users. Unlike the existing approaches that are often designed to train each module separately, we propose "UniConv" -- a novel unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues, which is designed to jointly train (i) a Bi-level State Tracker which tracks dialogue states by learning signals at both slot and domain level independently, and (ii) a Joint Dialogue Act and Response Generator which incorporates information from various input components and models dialogue acts and target responses simultaneously. We conduct comprehensive experiments in dialogue state tracking, context-to-text, and end-to-end settings on the MultiWOZ2.1 benchmark, achieving superior performance over competitive baselines.