# UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues

arXiv: https://arxiv.org/abs/2110.08032 · HTML: https://mlsanity.com/paper/2110.08032/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2](https://mlsanity.com/paper/2012.03539/) (2020)
- [One Model To Learn Them All](https://mlsanity.com/paper/1706.05137/) (2017)
- [PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning](https://mlsanity.com/paper/2006.16779/) (2020)
- [SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://mlsanity.com/paper/2005.05298/) (2020)
- [Adding Chit-Chat to Enhance Task-Oriented Dialogues](https://mlsanity.com/paper/2010.12757/) (2020)
- [The Adapter-Bot: All-In-One Controllable Conversational Model](https://mlsanity.com/paper/2008.12579/) (2020)
- [Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability](https://mlsanity.com/paper/1706.08476/) (2017)
- [Attention over Parameters for Dialogue Systems](https://mlsanity.com/paper/2001.01871/) (2020)

## Built on by (most cited first, 12 of 12)

- [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023)
- [Rewarding Chatbots for Real-World Engagement with Millions of Users](https://mlsanity.com/paper/2303.06135/) (2023)
- [Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History](https://mlsanity.com/paper/2503.05150/) (2025)
- [PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model](https://mlsanity.com/paper/2203.17090/) (2022)
- [Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog](https://mlsanity.com/paper/2212.10008/) (2022)
- [Mitigating Negative Style Transfer in Hybrid Dialogue System](https://mlsanity.com/paper/2212.07183/) (2022)
- [Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats](https://mlsanity.com/paper/2404.05012/) (2024)
- [Exploring Effective Information Utilization in Multi-Turn Topic-Driven Conversations](https://mlsanity.com/paper/2209.00250/) (2022)
- [Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues](https://mlsanity.com/paper/2307.01664/) (2023)
- [System-Initiated Transitions from Chit-Chat to Task-Oriented Dialogues with Transition Info Extractor and Transition Sentence Generator](https://mlsanity.com/paper/2308.03098/) (2023)
- [Healing Unsafe Dialogue Responses with Weak Supervision Signals](https://mlsanity.com/paper/2305.15757/) (2023)
- [JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning](https://mlsanity.com/paper/2309.00230/) (2023)

## Abstract (arXiv)

With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems. However, these two systems are often tackled separately in current methods. To achieve more natural interaction with humans, a dialogue agent needs to be capable of both chatting and accomplishing tasks. To this end, we propose a unified dialogue system (UniDS) with the two aforementioned skills. In particular, we design a unified dialogue data schema, compatible for both chit-chat and task-oriented dialogues, and we train UniDS with mixed dialogue data from a pretrained chit-chat dialogue model. Without adding extra parameters to SOTA baselines, UniDS can alternatively handle chit-chat and task-oriented dialogues in a unified framework. Experimental results demonstrate that the proposed UniDS works comparably well as the pure chit-chat system, and it outperforms state-of-the-art task-oriented dialogue systems. More importantly, UniDS achieves better robustness as it is able to smoothly switch between two types of dialogues. These results demonstrate the feasibility and potential of building an one-for-all dialogue system.