Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
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Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
| Challenge: | telemedicine is a medical practice that provides patient care remotely using video conferencing tools. |
| Approach: | They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance . |
| Outcome: | The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues. |
CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation (2022.emnlp-main)
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| Challenge: | Prior research has provided a single poorly graded label for the entire utterance, which may mislead model training and/or lead to erroneous assessment. |
| Approach: | They propose to use telemedicine to carry on a natural conversation and understand the meanings of words to respond with a coherent dialog. |
| Outcome: | telemedicine has been shown to be effective in carrying on a natural conversation and understanding the meanings of words to respond with a coherent dialog. |
Learning Health-Bots from Training Data that was Automatically Created using Paraphrase Detection and Expert Knowledge (2020.coling-main)
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| Challenge: | Current dialog models require large quantities of training data due to privacy issues . current methods require large amounts of training and are even scarcer in the health domain . |
| Approach: | They propose a method for creating dialog corpora from physician-patient interaction data . they combine expert knowledge provided by physicians with automatic paraphrase extraction techniques . |
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EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents (2026.findings-acl)
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| Challenge: | Existing medical dialogue corpora are largely dyadic or lack multi-party workflow and annotations needed for this setting. |
| Approach: | They propose an ePCR-grounded, topic-flow-based multi-agent generation pipeline that iteratively plans, generates, and self-refines dialogues with rule-based factual and topic flow checks. |
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DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |
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). |
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Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)
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Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
| Challenge: | a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms. |
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| Outcome: | The proposed dataset shows that it is useful in monolingual vs. multilingual settings. |
NoteChat: A Dataset of Synthetic Patient-Physician Conversations Conditioned on Clinical Notes (2024.findings-acl)
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| Challenge: | NoteChat is a cooperative multi-agent framework for generating patient-physician dialogues . evaluator finds it outperforms state-of-the-art models for generating clinical notes . clinical documentation is largely done by physicians at both steps . |
| Approach: | They propose a cooperative multi-agent framework leveraging Large Language Models to generate patient-physician dialogues. |
| Outcome: | The proposed framework outperforms state-of-the-art models for generating clinical notes . it can engage patients directly and help clinical documentation, a leading cause of physician burnout . |
Boosting Dialog Response Generation (P19-1)
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| Challenge: | Neural models generate the most common and generic responses all the time . Empirical results show that our method can significantly improve the diversity of responses generated by sequence-to-sequence models. |
| Approach: | They propose an iterative training process and ensemble method based on boosting to improve the diversity of responses generated by neural models. |
| Outcome: | Empirical results show that the proposed method significantly improves diversity and relevance of responses generated by all models. |
Controllable Dialogue Simulation with In-context Learning (2022.findings-emnlp)
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| Challenge: | Existing methods to generate annotated dialogues require crowdsourcing, which is expensive and time-consuming. |
| Approach: | They propose a dialogue simulation method based on large language model in-context learning that generates new dialogues and annotations in a controllable way. |
| Outcome: | The proposed method can expand a small set of dialogue data with minimum or zero human involvement and parameter update. |