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Found 8 papers, 2 papers with code
Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models
While the performance of the GPT model is relatively lower compared to the other approaches, we have observed that by employing few-shot learning techniques, we can enhance its results by up to 20\%.
MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare
MentalQA offers a valuable foundation for developing Arabic text mining tools capable of supporting mental health professionals and individuals seeking information.
Cluster-Level Contrastive Learning for Emotion Recognition in Conversations
To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL) method, which first reduces the high-dimensional SCL space to a three-dimensional affect representation space Valence-Arousal-Dominance (VAD), then performs cluster-level contrastive learning to incorporate measurable emotion prototypes.
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence.
Ranked #1 on
Emotion Classification
on SemEval 2018 Task 1E-c
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning
The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs).
UBC-NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models
We also offer an analysis of system performance and the impact of training data size on the task.
Enabling Deep Learning of Emotion With First-Person Seed Expressions
The computational treatment of emotion in natural language text remains relatively limited, and Arabic is no exception.
