Papers with MDD
Towards Efficient and Multifaceted Computer-assisted Pronunciation Training Leveraging Hierarchical Selective State Space Model and Decoupled Cross-entropy Loss (2025.naacl-long)
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| Challenge: | APA and MDD are two of the main tasks of computer-assisted pronunciation training (CAPT) systems. |
| Approach: | They propose a computer-assisted pronunciation training approach that integrates APA and MDD tasks in parallel. |
| Outcome: | The proposed approach improves on APA and MDD tasks, and achieves an F1 score of 63.85%. |
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony (2022.naacl-main)
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| Challenge: | Mental health disorders are one of the primary causes of disability worldwide . lack of qualified and competent mental health professionals is a major problem . we propose a virtual assistant that can act as the first point of contact and comfort for mental health patients. |
| Approach: | They propose a virtual assistant that can act as the first point of contact and comfort for mental health patients. |
| Outcome: | The proposed system outperforms baselines in the evaluation of 7k dyadic conversations from a peer-to-peer support platform. |
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers (2023.emnlp-main)
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| Challenge: | valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. |
| Approach: | They study the relationship between tweet emotion dynamics and mental health disorders by using a user-disclosed diagnosis. |
| Outcome: | The results show that the measures varied by the user's self-disclosed diagnosis. |
Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning (2024.naacl-long)
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| Challenge: | Bipolar Disorder (BD) is a mental disorder characterized by intense mood swings, ranging from depression to manic states. |
| Approach: | They propose to use social media data to identify BD risk in individuals misdiagnosed as MDD by multi-task learning. |
| Outcome: | The proposed approach outperforms state-of-the-art baselines and can provide insights into the impact of BD mood on future risk. |
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)
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Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang
| Challenge: | Existing approaches to learn a model from labeled data are expensive or prohibitive. |
| Approach: | They propose an unsupervised domain adaptation algorithm that leverages labeled data in a source domain to learn a well-performing model in . they use the Margin Disparity Discrepancy algorithm to optimize the margin loss on the source domain. |
| Outcome: | The proposed approach improves on a recent theoretical work on cross-lingual document classification and NER by a large margin. |
Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (2023.emnlp-main)
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| Challenge: | Existing mental disease detection methods are not backed by domain knowledge and thus fail to produce interpretable results. |
| Approach: | They propose a framework that can learn the shared clues of all diseases while also capturing the specificity of each single disease. |
| Outcome: | Experiments on the detection of 7 diseases show that the proposed model can boost detection performance by more than 10%, especially in relatively rare classes. |
Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media (2022.emnlp-main)
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| Challenge: | Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability due to lack of symptom modeling. |
| Approach: | They propose to annotate a social media corpus of symptom classes related to 7 mental disorders using a knowledge graph and a new annotation framework to facilitate further research. |
| Outcome: | The proposed model outperforms strong pure-text baselines and provides convincing MDD explanations with case studies. |