HaT5: Hate Language Identification using Text-to-Text Transfer Transformer

• URL: http://arxiv.org/abs/2202.05690v1
• Date: Fri, 11 Feb 2022 15:21:27 GMT
• Title: HaT5: Hate Language Identification using Text-to-Text Transfer Transformer
• Authors: Sana Sabah Sabry, Tosin Adewumi, Nosheen Abid, Gy\"orgy Kovacs, Foteini Liwicki and Marcus Liwicki
• Abstract summary: We investigate the performance of a state-of-the art (SoTA) architecture T5 across 5 different tasks from 2 relatively diverse datasets. To improve performance, we augment the training data by using an autoregressive model. It reveals the difficulties of poor data annotation by using a small set of examples.
• Score: 1.2532400738980594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the performance of a state-of-the art (SoTA) architecture T5 (available on the SuperGLUE) and compare with it 3 other previous SoTA architectures across 5 different tasks from 2 relatively diverse datasets. The datasets are diverse in terms of the number and types of tasks they have. To improve performance, we augment the training data by using an autoregressive model. We achieve near-SoTA results on a couple of the tasks - macro F1 scores of 81.66% for task A of the OLID 2019 dataset and 82.54% for task A of the hate speech and offensive content (HASOC) 2021 dataset, where SoTA are 82.9% and 83.05%, respectively. We perform error analysis and explain why one of the models (Bi-LSTM) makes the predictions it does by using a publicly available algorithm: Integrated Gradient (IG). This is because explainable artificial intelligence (XAI) is essential for earning the trust of users. The main contributions of this work are the implementation method of T5, which is discussed; the data augmentation using a new conversational AI model checkpoint, which brought performance improvements; and the revelation on the shortcomings of HASOC 2021 dataset. It reveals the difficulties of poor data annotation by using a small set of examples where the T5 model made the correct predictions, even when the ground truth of the test set were incorrect (in our opinion). We also provide our model checkpoints on the HuggingFace hub1 to foster transparency.

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