AraNet: A Deep Learning Toolkit for Arabic Social Media
원문 초록
We describe AraNet, a collection of deep learning Arabic social media processing tools. Namely, we exploit an extensive host of publicly available and novel social media datasets to train bidirectional encoders from transformer models (BERT) to predict age, dialect, gender, emotion, irony, and sentiment. AraNet delivers state-of-the-art performance on a number of the cited tasks and competitively on others. In addition, AraNet has the advantage of being exclusively based on a deep learning framework and hence feature engineering free. To the best of our knowledge, AraNet is the first to performs predictions across such a wide range of tasks for Arabic NLP and thus meets a critical needs. We publicly release AraNet to accelerate research and facilitate comparisons across the different tasks.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging
CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data
Enhancing Abstractive Summarization of Scientific Papers Using Structure Information
GSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media Steganalysis
토론
이 논문과 연결된 포럼 글이 아직 없습니다.