| Challenge: | AV-Dialog uses audio and visual cues to track the target speaker, predict turn-taking, and generate coherent responses. |
| Approach: | They propose a multimodal dialog framework that uses both audio and visual cues to track the target speaker. |
| Outcome: | AV-Dialog outperforms audio-only models under interference, reducing transcription errors, improving turn-taking prediction and human-rated dialogue quality. |
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Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He
| Challenge: | End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored. |
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Game-Based Video-Context Dialogue (D18-1)
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| Challenge: | Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. |
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DialogGen: Multi-modal Interactive Dialogue System with Multi-turn Text-Image Generation (2025.findings-naacl)
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Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu
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ZipVoice-Dialog: Non-Autoregressive Spoken Dialogue Generation with Flow Matching (2026.findings-acl)
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Han Zhu, Wei Kang, Liyong Guo, Zengwei Yao, Fangjun Kuang, Weiji Zhuang, Zhaoqing Li, Zhifeng Han, Dong Zhang, Xin Zhang, Xingchen Song, Lingxuan Ye, Long Lin, Daniel Povey
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Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)
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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang
| Challenge: | Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments. |
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Behavior-SD: Behaviorally Aware Spoken Dialogue Generation with Large Language Models (2025.naacl-long)
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| Challenge: | Spoken dialogues lack explicit modeling of behavior traits that are often overlooked in language models . et al.: our work opens new possibilities for developing behaviorally-aware dialogue systems . |
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Construction and Analysis of a Multimodal Chat-talk Corpus for Dialog Systems Considering Interpersonal Closeness (2020.lrec-1)
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| Challenge: | a large-scale multimodal dialog corpus is needed to accelerate research on dialog systems that can handle social signals and verbal information. |
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The World in My Mind: Visual Dialog with Adversarial Multi-modal Feature Encoding (N19-1)
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| Challenge: | Visual Dialog is a multi-modal task that requires a model to participate in a dialog grounded on an image and generate correct, human-like responses. |
| Approach: | They propose a framework for effective and robust auxiliary training of visual dialog systems using multi-modal encoding. |
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Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning (2025.emnlp-main)
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| Challenge: | Existing multimodal large language models are trained on single-turn vision question-answering tasks, which do not accurately reflect real-world human conversations. |
| Approach: | They propose a large-scale multi-turn multimodal dialogue dataset that uses rules and GPT assistance to generate a multi-turned multimodal dialog dataset. |
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HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data (2022.findings-acl)
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| Challenge: | Existing datasets focused on multiturn dialogue systems focus on text or table information. |
| Approach: | They propose a dataset that consists of crowdsourced conversations grounded on Wikipedia text and tables. |
| Outcome: | The proposed dataset shows that there is still ample opportunity for improvement in the current state of dialogue systems. |