Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on image-sharing behavior in singular sessions, leading to limited long-term social interaction. |
| Approach: | They propose a large-scale long-term multi-modal dialogue dataset that generates long-time multi-modity dialogue distilled from ChatGPT and proposed image aligner. |
| Outcome: | The proposed framework generates long-term multi-modal dialogue from ChatGPT and image aligner. |
Similar Papers
SHARE: Shared Memory-Aware Open-Domain Long-Term Dialogue Dataset Constructed from Movie Script (2025.acl-long)
Copied to clipboard
| Challenge: | Antoine de Saint-Exupéry Memory in dialogue plays a crucial role in building relationships and facilitating the ongoing conversation. |
| Approach: | They propose a long-term dialogue dataset named SHARE that includes shared memories between two individuals. |
| Outcome: | The proposed dataset makes long-term dialogues more engaging and sustainable . it includes summaries of persona information and events of two individuals . |
MMChat: Multi-Modal Chat Dataset on Social Media (2022.lrec-1)
Copied to clipboard
| Challenge: | Incorporating multi-modal contexts in conversation is important for developing engaging dialogue systems. |
| Approach: | They propose a large scale Chinese multi-modal dialogue corpus that contains image-grounded dialogues from real conversations on social media. |
| Outcome: | The proposed model can handle sparsity issues in dialogue generation tasks by incorporating image features. |
Multi-Modal Open-Domain Dialogue (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size. |
| Approach: | They combine open-domain dialogue agents with vision models to investigate human preferences and humanness. |
| Outcome: | The proposed model outperforms existing models in multi-modal dialogue while performing as well as its predecessor (text-only) BlenderBot. |
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
Copied to clipboard
| Challenge: | Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality. |
| Approach: | They propose a framework that leverages commonsense-based persona expansion to address such issues in long-term conversations. |
| Outcome: | The proposed framework facilitates better response generation via human-like persona refinement. |
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images (2021.acl-short)
Copied to clipboard
| Challenge: | Existing training methods for multi-modal dialogue systems rely on image captioning or visual question answering datasets that are irrelevant to the dialogue context. |
| Approach: | They propose to create a 45k multi-modal dialogue dataset with minimal human intervention . they use text dialogue datasets, image-mixed dialogues and contextual-similarity filtering . |
| Outcome: | The proposed dataset can be used as training data for multi-modal dialogue systems . human evaluations show that the model can be effectively used . |
Post Persona Alignment for Multi-Session Dialogue Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for multi-session persona-based dialogue generation typically retrieve persona information before response generation, which can constrain diversity and result in generic outputs. |
| Approach: | They propose a two-stage framework that reverses the process of retrieving persona information before response generation. |
| Outcome: | Experiments on multi-session persona-based dialogue data show that the proposed framework outperforms existing methods in consistency, diversity, and persona relevance. |
Long Time No See! Open-Domain Conversation with Long-Term Persona Memory (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing persona dialogue datasets and models can build long-term relationships with humans . however, current open-domain dialogue systems cannot build long relationships with users . |
| Approach: | They propose a long-term memory conversation dataset and a dialogue generation framework with long-Term memory mechanism to extract and continuously update long-time persona memory. |
| Outcome: | The proposed system outperforms baselines in terms of long-term dialogue consistency . the proposed system can build long-lasting relationships between humans and bots . |
LongMP-Bench: A Benchmark for Multimodal Persona Understanding in Long-Term Dialogues (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets suffer from limited persona diversity and static, overly simplified settings, making them insufficient for capturing the complexity of real-world interactions. |
| Approach: | They propose a benchmark to evaluate models' ability to understand evolving user personas within long-term multimodal dialogues by using a dataset that contains long conversations from 150 users. |
| Outcome: | The proposed benchmark aims to assess models' ability to track persona evolution, integrate visual and textual inputs, and apply persona understanding in realistic dialogue scenarios. |
MPCHAT: Towards Multimodal Persona-Grounded Conversation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing research on persona-based dialogue has focused on textual persona that delivers personal facts or personalities, but image modality can reveal the speaker’s personal characteristics and experiences in episodic memory. |
| Approach: | They propose a multimodal persona-based dialogue dataset which extends persona with both text and images to contain episodic memories. |
| Outcome: | The proposed dataset extends persona with text and images to contain episodic memories. |
DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on the self and inter-personal dependencies in multi-modal conversations, but they ignore the temporal and spatial dependencies. |
| Approach: | They propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. |
| Outcome: | The proposed models outperform the state-of-the-art on three benchmark datasets. |