Mengyue Wu


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D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat
Binwei Yao | Chao Shi | Likai Zou | Lingfeng Dai | Mengyue Wu | Lu Chen | Zhen Wang | Kai Yu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms based on clinical diagnosis criteria. Such a dialogue system is distinguished from existing single-purpose human-machine dialog systems, as it combines task-oriented and chit-chats with uniqueness in dialogue topics and procedures. However, due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. Based on clinical depression diagnostic criteria ICD-11 and DSM-5, we designed a 3-phase procedure to construct D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat, which simulates the dialogue between doctors and patients during the diagnosis of depression, including diagnosis results and symptom summary given by professional psychiatrists for each conversation. Upon the newly-constructed dataset, four tasks mirroring the depression diagnosis process are established: response generation, topic prediction, dialog summary, and severity classification of depressive episode and suicide risk. Multi-scale evaluation results demonstrate that a more empathy-driven and diagnostic-accurate consultation dialogue system trained on our dataset can be achieved compared to rule-based bots.

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Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media
Zhiling Zhang | Siyuan Chen | Mengyue Wu | Kenny Zhu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the first annotated symptom identification corpus of multiple psychiatric disorders, to facilitate further research progress. PsySym is annotated according to a knowledge graph of the 38 symptom classes related to 7 mental diseases complied from established clinical manuals and scales, and a novel annotation framework for diversity and quality. Experiments show that symptom-assisted MDD enabled by PsySym can outperform strong pure-text baselines. We also exhibit the convincing MDD explanations provided by symptom predictions with case studies, and point to their further potential applications.


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Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL
Zhi Chen | Lu Chen | Hanqi Li | Ruisheng Cao | Da Ma | Mengyue Wu | Kai Yu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021