Arun Rajendran
2019
UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data
Arun Rajendran
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Chiyu Zhang
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Muhammad Abdul-Mageed
Proceedings of the 13th International Workshop on Semantic Evaluation
We examine learning offensive content on Twitter with limited, imbalanced data. For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. Among the 75 participating teams in SemEval-2019 sub-task B, our system ranks 6th (with 0.706 macro F1-score). For sub-task C, among the 65 participating teams, our system ranks 9th (with 0.587 macro F1-score).
UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs
Chiyu Zhang
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Arun Rajendran
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Muhammad Abdul-Mageed
Proceedings of the 13th International Workshop on Semantic Evaluation
We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3% accuracy) on the ‘labels-by-publisher’ sub-task and top 24 out of 44 teams (68.3% accuracy) on the ‘labels-by-article’ sub-task (65.3% accuracy). We also report a model that scores higher than the 8th ranking system (78.5% accuracy) on the ‘labels-by-article’ sub-task.
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