Summary of Arabic Tweet Act: a Weighted Ensemble Pre-trained Transformer Model For Classifying Arabic Speech Acts on Twitter, by Khadejaa Alshehri et al.
Arabic Tweet Act: A Weighted Ensemble Pre-Trained Transformer Model for Classifying Arabic Speech Acts on Twitter
by Khadejaa Alshehri, Areej Alhothali, Nahed Alowidi
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to classifying speech acts in Twitter dialectal Arabic using transformer deep learning neural networks. By leveraging the strengths of various BERT models, the authors develop a weighted ensemble learning method that integrates the advantages of different models for improved performance. To overcome class imbalance issues common in speech act problems, the authors implement a data augmentation model using transformers to generate an equal proportion of speech act categories. The results show that the best BERT model is araBERTv2-Twitter, achieving a macro-averaged F1 score and accuracy of 0.73 and 0.84, respectively. Furthermore, the authors demonstrate improved performance using a BERT-based ensemble method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how people talk on social media in Arabic. Speech acts are what people do when they say something – like asking or thanking someone. To do this, the researchers used special computer programs called transformer deep learning neural networks. They combined different models to make a better one that can recognize different types of speech acts, even when there aren’t many examples of each type. This is important because social media is becoming more important for sharing ideas and understanding what people think. |
Keywords
» Artificial intelligence » Bert » Data augmentation » Deep learning » F1 score » Transformer
