From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection

• URL: http://arxiv.org/abs/2102.12162v1
• Date: Wed, 24 Feb 2021 09:30:55 GMT
• Title: From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection
• Authors: Quang Huu Pham, Viet Anh Nguyen, Linh Bao Doan, Ngoc N. Tran and Ta Minh Thanh
• Abstract summary: We propose a pipeline to adapt the general-purpose RoBERTa language model to a specific text classification task: Vietnamese Hate Speech Detection. Our experiments proved that our proposed pipeline boosts the performance significantly, achieving a new state-of-the-art on Vietnamese Hate Speech Detection campaign with 0.7221 F1 score.
• Score: 8.602181445598776
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Natural language processing is a fast-growing field of artificial intelligence. Since the Transformer was introduced by Google in 2017, a large number of language models such as BERT, GPT, and ELMo have been inspired by this architecture. These models were trained on huge datasets and achieved state-of-the-art results on natural language understanding. However, fine-tuning a pre-trained language model on much smaller datasets for downstream tasks requires a carefully-designed pipeline to mitigate problems of the datasets such as lack of training data and imbalanced data. In this paper, we propose a pipeline to adapt the general-purpose RoBERTa language model to a specific text classification task: Vietnamese Hate Speech Detection. We first tune the PhoBERT on our dataset by re-training the model on the Masked Language Model task; then, we employ its encoder for text classification. In order to preserve pre-trained weights while learning new feature representations, we further utilize different training techniques: layer freezing, block-wise learning rate, and label smoothing. Our experiments proved that our proposed pipeline boosts the performance significantly, achieving a new state-of-the-art on Vietnamese Hate Speech Detection campaign with 0.7221 F1 score.

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