# Profile Consistency Identification for Open-domain Dialogue Agents

arXiv: https://arxiv.org/abs/2009.09680 · HTML: https://mlsanity.com/paper/2009.09680/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 22 of 22)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://mlsanity.com/paper/2106.06169/) (2021)
- [CharacterChat: Learning towards Conversational AI with Personalized Social Support](https://mlsanity.com/paper/2308.10278/) (2023)
- [MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation](https://mlsanity.com/paper/2211.05719/) (2022)
- [The Oscars of AI Theater: A Survey on Role-Playing with Language Models](https://mlsanity.com/paper/2407.11484/) (2024)
- [Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods](https://mlsanity.com/paper/2203.05227/) (2022)
- [Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona](https://mlsanity.com/paper/2305.11482/) (2023)
- [MPCHAT: Towards Multimodal Persona-Grounded Conversation](https://mlsanity.com/paper/2305.17388/) (2023)
- [Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System](https://mlsanity.com/paper/2109.11292/) (2021)
- [Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters](https://mlsanity.com/paper/2211.06869/) (2022)
- [ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human](https://mlsanity.com/paper/2304.07849/) (2023)
- [Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency](https://mlsanity.com/paper/2106.02228/) (2021)
- [SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation](https://mlsanity.com/paper/2305.11130/) (2023)
- [CDConv: A Benchmark for Contradiction Detection in Chinese Conversations](https://mlsanity.com/paper/2210.08511/) (2022)
- ["In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning](https://mlsanity.com/paper/2403.03102/) (2024)
- [Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding](https://mlsanity.com/paper/2112.11953/) (2021)
- [When Memory Updates but Behavior Does Not: Repairing Implicit Stale Dependencies in Personalized Agent Responses](https://mlsanity.com/paper/2608.01619/) (2026)
- [Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing](https://mlsanity.com/paper/2409.16913/) (2024)
- [PICon: A Multi-Turn Interrogation Framework for Evaluating Persona Agent Consistency](https://mlsanity.com/paper/2603.25620/) (2026)
- [Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona](https://mlsanity.com/paper/2109.08875/) (2021)
- [A Heterogeneous Temporal Memory Governance Framework for Long-Term LLM Persona Consistency](https://mlsanity.com/paper/2605.14802/) (2026)
- [A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generation](https://mlsanity.com/paper/2410.20174/) (2024)

## Abstract (arXiv)

Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans. Existing studies on improving attribute consistency mainly explored how to incorporate attribute information in the responses, but few efforts have been made to identify the consistency relations between response and attribute profile. To facilitate the study of profile consistency identification, we create a large-scale human-annotated dataset with over 110K single-turn conversations and their key-value attribute profiles. Explicit relation between response and profile is manually labeled. We also propose a key-value structure information enriched BERT model to identify the profile consistency, and it gained improvements over strong baselines. Further evaluations on downstream tasks demonstrate that the profile consistency identification model is conducive for improving dialogue consistency.