1 code implementation • 8 Apr 2023 • Lama Alkhaled, Tosin Adewumi, Sana Sabah Sabry
We introduce bipol, a new metric with explainability, for estimating social bias in text data.
2 code implementations • 28 Jan 2023 • Tosin Adewumi, Isabella Södergren, Lama Alkhaled, Sana Sabah Sabry, Foteini Liwicki, Marcus Liwicki
Hence, we also contribute a new, large Swedish bias-labelled dataset (of 2 million samples), translated from the English version and train the SotA mT5 model on it.
no code implementations • 11 Oct 2022 • Tosin Adewumi, Sana Sabah Sabry, Nosheen Abid, Foteini Liwicki, Marcus Liwicki
Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods like data augmentation and ensemble may have on the best model, if any.
no code implementations • 11 Feb 2022 • Sana Sabah Sabry, Tosin Adewumi, Nosheen Abid, György Kovacs, Foteini Liwicki, Marcus Liwicki
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.
no code implementations • 12 Oct 2021 • Tosin Adewumi, Rickard Brännvall, Nosheen Abid, Maryam Pahlavan, Sana Sabah Sabry, Foteini Liwicki, Marcus Liwicki
Perplexity score (an automated intrinsic language model metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models, with results that indicate that the capacity for transfer learning can be exploited with considerable success.