Search Results for author:
Found 8 papers, 3 papers with code
Sentence Contextual Encoder with BERT and BiLSTM for Automatic Classification with Imbalanced Medication Tweets
This paper details the system description and approach used by our team for the SMM4H 2020 competition, Task 1.
Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generation
To leverage information from text pairs, we additionally introduce a novel supervised model we call dual directional learning (DDL), which is designed to integrate with our proposed VSAR model.
Religion and Spirituality on Social Media in the Aftermath of the Global Pandemic
During the COVID-19 pandemic, the Church closed its physical doors for the first time in about 800 years, which is, arguably, a cataclysmic event.
Incorporating Emotions into Health Mention Classification Task on Social Media
The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text.
Multi-task Learning for Personal Health Mention Detection on Social Media
Detecting personal health mentions on social media is essential to complement existing health surveillance systems.
INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations
XAI with natural language processing aims to produce human-readable explanations as evidence for AI decision-making, which addresses explainability and transparency.
Decision Making
Explainable Artificial Intelligence (XAI)
+2
Colonoscopy polyp detection with massive endoscopic images
We improved an existing end-to-end polyp detection model with better average precision validated by different data sets with trivial cost on detection speed.
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums
Learners may post their feelings of confusion and struggle in the respective MOOC forums, but with the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention.
