Papers with Dialogue models
Retrieval-free Knowledge Injection through Multi-Document Traversal for Dialogue Models (2023.acl-long)
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Rui Wang, Jianzhu Bao, Fei Mi, Yi Chen, Hongru Wang, Yasheng Wang, Yitong Li, Lifeng Shang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Existing research on retrieval-augmented and retrieval free dialogue models focuses on retrieving knowledge from external sources and rely on finely annotated retrieval training data and knowledge-grounded responses. |
| Approach: | They propose a retrieval-free approach by turning knowledge documents into simulated multi-turn dialogues using a Multi-Document Traversal algorithm. |
| Outcome: | The proposed approach outperforms retrieval-augmented models while being cheaper and faster at domain transfer. |
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts (2021.emnlp-main)
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| Challenge: | despite progress toward data-driven conversational agents, dialogue models still suffer from issues surrounding safety and offensive language. |
| Approach: | They analyze reddit threads and reddits to determine the stance of offensive dialogue models . they find 42% of human responses agree with toxic comments, compared to 13% with safe comments . |
| Outcome: | The proposed model produces 29% fewer offensive replies than the baseline model. |
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2023.findings-emnlp)
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Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur
| Challenge: | Dialogue models are able to generate fluent and interesting responses, but they can be difficult to control and may produce non-engaging, unsafe results. |
| Approach: | They propose a framework for controlling dialogue model behavior using natural language rules, or guidelines, which provide information about the context they are applicable to and what should be included in the response. |
| Outcome: | The proposed framework is effective in three open-domain dialogue response generation tasks and is consistent with the developer's expectations and intent. |
AV-Dialog: Spoken Dialogue Models with Audio-Visual Input (2026.acl-long)
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| 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. |