Papers with DailyDialog
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis (2022.coling-1)
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| Challenge: | Emotion Recognition in Conversation (ERC) has attracted increasing research attention in recent years. |
| Approach: | They propose to model the emotional interactions between speakers to simulate the emotional inertia, emotional stimulus, global and local emotional evolution in dialogues. |
| Outcome: | The proposed model can achieve superior performance compared to state-of-the-art methods on four ERC benchmark datasets, IEMOCAP, MELD, EmoryNLP and DailyDialog. |
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (2022.acl-long)
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Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan
| Challenge: | Existing pre-trained dialog models shed light on various downstream tasks in natural language processing (NLP). |
| Approach: | They propose a dialog pre-training framework that introduces latent variables into the enhanced encoder-decoder pre-train framework to increase relevance and diversity of responses. |
| Outcome: | The proposed model achieves state-of-the-art on personaChat, DailyDialog, and DSTC7-AVSD datasets. |
Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS (2026.findings-acl)
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| Challenge: | Existing systems lack a self-emotion determination mechanism to drive the streaming text-to-speech (TTS) synthesis. |
| Approach: | They propose an emotion-planning framework that determines the emotion prior to the textual generation, grounding the downstream emotional TTS in a streaming manner. |
| Outcome: | The proposed framework outperforms baselines on DailyDialog, EmoryNLP, IMEOCAP, and MELD on emotional alignment, contextual coherence, and expressive fluency. |
ECO Decoding: Entropy-Based Control for Controllability and Fluency in Controllable Dialogue Generation (2025.emnlp-main)
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| Challenge: | Controllable Dialogue Generation (CDG) enables chatbots to generate responses tailored to desired attributes like emotion and dialog-act. |
| Approach: | They propose a method which dynamically adjusts the control strength at each generation step according to the model’s entropy in both the language model and attribute classifier probability distributions. |
| Outcome: | The proposed method outperforms existing methods on DailyDialog and MultiWOZ datasets while maintaining fluency and grammar. |
Reading Between the Lines: The One-Sided Conversation Problem (2026.findings-acl)
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| Challenge: | In many real-world scenarios, only one side of a conversation is available for processing. |
| Approach: | They propose a one-sided conversation problem to reconstruct the missing speaker's turns and generate faithful summaries from one-side transcripts. |
| Outcome: | The proposed model improves reconstructions with prompting, but smaller models require fine tuning. |