Are Current Task-oriented Dialogue Systems Able to Satisfy Impolite Users?
Not on the map. Why?
Abstract (arXiv)
Task-oriented dialogue (TOD) systems have assisted users on many tasks, including ticket booking and service inquiries. While existing TOD systems have shown promising performance in serving customer needs, these systems mostly assume that users would interact with the dialogue agent politely. This assumption is unrealistic as impatient or frustrated customers may also interact with TOD systems impolitely. This paper aims to address this research gap by investigating impolite users' effects on TOD systems. Specifically, we constructed an impolite dialogue corpus and conducted extensive experiments to evaluate the state-of-the-art TOD systems on our impolite dialogue corpus. Our experimental results show that existing TOD systems are unable to handle impolite user utterances. We also present a data augmentation method to improve TOD performance in impolite dialogues. Nevertheless, handling impolite dialogues remains a very challenging research task. We hope by releasing the impolite dialogue corpus and establishing the benchmark evaluations, more researchers are encouraged to investigate this new challenging research task.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 59 references that are in the map. First posted on arXiv 24 Oct 2022.
Builds on
37 of 59 references.
References · 37
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
- 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 426
- 2020A Simple Language Model for Task-Oriented Dialoguecited by 331
- 2020MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselinescited by 201
- 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
- 2017Key-Value Retrieval Networks for Task-Oriented Dialoguecited by 137
- 2017A Knowledge-Grounded Neural Conversation Modelcited by 136
- 2019Survey on Evaluation Methods for Dialogue Systemscited by 126
25 more
- 2020UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2cited by 111
- 2013A Computational Approach to Politeness with Application to Social Factorscited by 104
- 2016Learning End-to-End Goal-Oriented Dialogcited by 104
- 2020MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systemscited by 103
- 2019Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Contextcited by 99
- 2020Politeness Transfer: A Tag and Generate Approachcited by 78
- 2018Sequence-to-Sequence Data Augmentation for Dialogue Language Understandingcited by 67
- 2020CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Datasetcited by 66
- 2018Polite Dialogue Generation Without Parallel Datacited by 57
- 2016A User Simulator for Task-Completion Dialoguescited by 56
- 2021Shades of BLEU, Flavours of Success: The Case of MultiWOZcited by 48
- 2020A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learningcited by 47
- 2021Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systemscited by 47
- 2019Structured Fusion Networks for Dialogcited by 40
- 2020RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systemscited by 36
- 2019Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialoguecited by 35
- 2020Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systemscited by 35
- 2021AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Modelscited by 31
- 2016Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learningcited by 29
- 2017Iterative Policy Learning in End-to-End Trainable Task-Oriented Neural Dialog Modelscited by 26
- 2019Domain Adaptive Text Style Transfercited by 25
- 2017Flexible End-to-End Dialogue System for Knowledge Grounded Conversationcited by 18
- 2018Sentiment Adaptive End-to-End Dialog Systemscited by 17
- 2020Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue Managementcited by 13
- 2018Towards Dialogue-based Navigation with Multivariate Adaptation driven by Intention and Politeness for Social Robotscited by 3
Built on by
Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2023From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialoguecited by 3
- 2023Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfercited by 2
- 2026WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agentscited by 1
- 2026WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents1 Jun 2026
- 2023From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue24 Aug 2023
- 2023Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer10 May 2023