Overlapping Word Removal is All You Need: Revisiting Data Imbalance in Hope Speech Detection

Hariharan RamakrishnaIyer LekshmiAmmal; Manikandan Ravikiran; Gayathri Nisha; Navyasree Balamuralidhar; Adithya Madhusoodanan; Anand Kumar Madasamy; Bharathi Raja Chakravarthi

doi:10.1080/0952813x.2023.2166130

arXiv · 2204.05488

Overlapping Word Removal is All You Need: Revisiting Data Imbalance in Hope Speech Detection

Hariharan RamakrishnaIyer LekshmiAmmal·Manikandan Ravikiran·Gayathri Nisha·Navyasree Balamuralidhar·Adithya Madhusoodanan·Anand Kumar Madasamy·Bharathi Raja Chakravarthi

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Abstract

Hope Speech Detection, a task of recognizing positive expressions, has made significant strides recently. However, much of the current works focus on model development without considering the issue of inherent imbalance in the data. Our work revisits this issue in hope-speech detection by introducing focal loss, data augmentation, and pre-processing strategies. Accordingly, we find that introducing focal loss as part of Multilingual-BERT's (M-BERT) training process mitigates the effect of class imbalance and improves overall F1-Macro by 0.11. At the same time, contextual and back-translation-based word augmentation with M-BERT improves results by 0.10 over baseline despite imbalance. Finally, we show that overlapping word removal based on pre-processing, though simple, improves F1-Macro by 0.28. In due process, we present detailed studies depicting various behaviors of each of these strategies and summarize key findings from our empirical results for those interested in getting the most out of M-BERT for hope speech detection under real-world conditions of data imbalance.

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Hariharan RamakrishnaIyer LekshmiAmmal, Manikandan Ravikiran, Gayathri Nisha, Navyasree Balamuralidhar, Adithya Madhusoodanan, Anand Kumar Madasamy, Bharathi Raja Chakravarthi. 2022-04-12. Overlapping Word Removal is All You Need: Revisiting Data Imbalance in Hope Speech Detection. https://doi.org/10.1080/0952813x.2023.2166130

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