Papers by Jiao Ou
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response. |
| Approach: | They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples. |
| Outcome: | The proposed model outperforms baseline models in producing coherent and empathetic responses. |
DialogBench: Evaluating LLMs as Human-like Dialogue Systems (2024.naacl-long)
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| Challenge: | Existing benchmarks only evaluate LLMs' abilities for task completion as assistant AI. |
| Approach: | They propose a dialogue evaluation benchmark that contains 12 dialogue tasks to evaluate LLMs' capabilities as human-like dialogue systems. |
| Outcome: | The proposed benchmark contains 12 tasks to evaluate LLMs' capabilities . it shows that instruction tuning improves human likeness, but not as human-like systems . |
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (2022.emnlp-main)
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| Challenge: | Existing methods to augment dialogue datasets are labor-intensive and time-consuming . Currently, smoking is harmful for your health. |
| Approach: | They propose a data augmentation method to augment dialogue responses with different semantics by counterfactual inference. |
| Outcome: | The proposed method outperforms baselines on multiple downstream tasks. |
Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues (2024.emnlp-main)
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| Challenge: | Existing methods target instruction dialogues as learning goal and fine-tune user simulators to pose instructions. |
| Approach: | They propose to use real instruction dialogues to model complex dialogue flows and pose high-quality instructions. |
| Outcome: | The proposed method generates diverse, in-depth, and insightful instructions for a given dialogue history. |