# KETOD: Knowledge-Enriched Task-Oriented Dialogue

arXiv: https://arxiv.org/abs/2205.05589 · HTML: https://mlsanity.com/paper/2205.05589/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017)
- [Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://mlsanity.com/paper/1508.01745/) (2015)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://mlsanity.com/paper/1506.06714/) (2015)
- [ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons](https://mlsanity.com/paper/1909.03087/) (2019)
- [SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://mlsanity.com/paper/2005.05298/) (2020)
- [Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access](https://mlsanity.com/paper/2006.03533/) (2020)
- [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1804.06512/) (2018)
- [Adding Chit-Chat to Enhance Task-Oriented Dialogues](https://mlsanity.com/paper/2010.12757/) (2020)
- [Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/1704.03084/) (2017)
- [Global-Locally Self-Attentive Dialogue State Tracker](https://mlsanity.com/paper/1805.09655/) (2018)
- [DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs](https://mlsanity.com/paper/1910.00610/) (2019)
- ["How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations](https://mlsanity.com/paper/2109.13489/) (2021)
- [Fusing task-oriented and open-domain dialogues in conversational agents](https://mlsanity.com/paper/2109.04137/) (2021)
- [End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1711.10712/) (2017)
- [Latent Intention Dialogue Models](https://mlsanity.com/paper/1705.10229/) (2017)

## 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)
- [Towards Human-centered Proactive Conversational Agents](https://mlsanity.com/paper/2404.12670/) (2024)
- [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://mlsanity.com/paper/2307.10172/) (2023)
- ["What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge](https://mlsanity.com/paper/2305.12091/) (2023)
- [OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience](https://mlsanity.com/paper/2206.12449/) (2022)
- [Knowledge-grounded Dialog State Tracking](https://mlsanity.com/paper/2210.06656/) (2022)
- [Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog](https://mlsanity.com/paper/2212.10008/) (2022)
- [UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt](https://mlsanity.com/paper/2309.11065/) (2023)
- [A Unified Approach to Emotion Detection and Task-Oriented Dialogue Modeling](https://mlsanity.com/paper/2401.13789/) (2024)
- [Enhancing Task-Oriented Dialogues with Chitchat: a Comparative Study Based on Lexical Diversity and Divergence](https://mlsanity.com/paper/2311.14067/) (2023)
- [From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems](https://mlsanity.com/paper/2212.03279/) (2022)
- [CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking](https://mlsanity.com/paper/2610.02460/) (2026)

## Abstract (arXiv)

Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. Towards building a human-like assistant that can converse naturally and seamlessly with users, it is important to build a dialogue system that conducts both types of conversations effectively. In this work, we investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model. To this end, we create a new dataset, KETOD (Knowledge-Enriched Task-Oriented Dialogue), where we naturally enrich task-oriented dialogues with chit-chat based on relevant entity knowledge. We also propose two new models, SimpleToDPlus and Combiner, for the proposed task. Experimental results on both automatic and human evaluations show that the proposed methods can significantly improve the performance in knowledge-enriched response generation while maintaining a competitive task-oriented dialog performance. We believe our new dataset will be a valuable resource for future studies. Our dataset and code are publicly available at urlhttps://github.com/facebookresearch/ketod.