mlsanityData to 7 Oct 2026

UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts

arXiv 2304.04539 ↗First posted 10 Apr 2023cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

Given the current state of the world, because of existing situations around the world, millions of people suffering from mental illnesses feel isolated and unable to receive help in person. Psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. People have increasingly turned to online platforms to express themselves and seek help with their conditions. Deep learning methods have been commonly used to identify and analyze mental health conditions from various sources of information, including social media. Still, they face challenges, including a lack of reliability and overconfidence in predictions resulting in the poor calibration of the models. To solve these issues, We propose UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing reliable and well-calibrated predictions to classify six possible types of mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD by analyzing unstructured user data on Reddit.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 47 of its 72 references that are in the map. First posted on arXiv 10 Apr 2023.

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References · 47

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2017On Calibration of Modern Neural Networkscited by 4,860
  6. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  7. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  8. 2014Going Deeper with Convolutionscited by 4,231
  9. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  10. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  11. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  12. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
35 more
  1. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  2. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  3. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  4. 2017Deep Neural Networks as Gaussian Processescited by 617
  5. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  6. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
  7. 2020Pretrained Transformers Improve Out-of-Distribution Robustnesscited by 293
  8. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 278
  9. 2017PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examplescited by 243
  10. 2018Understanding Measures of Uncertainty for Adversarial Example Detectioncited by 177
  11. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  12. 2019Good-Enough Compositional Data Augmentationcited by 158
  13. 2017Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samplescited by 147
  14. 2020Better Aggregation in Test-Time Augmentationcited by 121
  15. 2016Data Recombination for Neural Semantic Parsingcited by 111
  16. 2018Uncertainty in Neural Networks: Approximately Bayesian Ensemblingcited by 94
  17. 2019Calibration tests in multi-class classification: A unifying frameworkcited by 79
  18. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 75
  19. 2018Deflecting Adversarial Attacks with Pixel Deflectioncited by 71
  20. 2019Generalized Data Augmentation for Low-Resource Translationcited by 53
  21. 2019Data Augmentation via Dependency Tree Morphing for Low-Resource Languagescited by 45
  22. 2019Analyzing the Role of Model Uncertainty for Electronic Health Recordscited by 41
  23. 2019XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answeringcited by 35
  24. 2017Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemblecited by 23
  25. 2019An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answeringcited by 22
  26. 2019Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answeringcited by 21
  27. 2019Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewritingcited by 12
  28. 2019Transforming Wikipedia into Augmented Data for Query-Focused Summarizationcited by 12
  29. 2019Controllable Data Synthesis Method for Grammatical Error Correctioncited by 8
  30. 2022Uncertainty-aware deep learning methods for robust diabetic retinopathy classificationcited by 8
  31. 2022Improved Text Classification via Test-Time Augmentationcited by 6
  32. 2020Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensemblescited by 5
  33. 2022Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learningcited by 5
  34. 2021Uncertainty-Aware Boosted Ensembling in Multi-Modal Settingscited by 4
  35. 2021Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Datacited by 2

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