Lama Alkhaled
2023
Bipol: Multi-Axes Evaluation of Bias with Explainability in Benchmark Datasets
Tosin Adewumi
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Isabella Södergren
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Lama Alkhaled
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Sana Al-azzawi
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Foteini Simistira Liwicki
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Marcus Liwicki
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
We investigate five English NLP benchmark datasets (on the superGLUE leaderboard) and two Swedish datasets for bias, along multiple axes. The datasets are the following: Boolean Question (Boolq), CommitmentBank (CB), Winograd Schema Challenge (WSC), Winogender diagnostic (AXg), Recognising Textual Entailment (RTE), Swedish CB, and SWEDN. Bias can be harmful and it is known to be common in data, which ML models learn from. In order to mitigate bias in data, it is crucial to be able to estimate it objectively. We use bipol, a novel multi-axes bias metric with explainability, to estimate and explain how much bias exists in these datasets. Multilingual, multi-axes bias evaluation is not very common. 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. In addition, we contribute new multi-axes lexica for bias detection in Swedish. We make the codes, model, and new dataset publicly available.
2022
ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language
Tosin Adewumi
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Lama Alkhaled
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Hamam Mokayed
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Foteini Liwicki
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Marcus Liwicki
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. Our system consists of finetuning a pretrained text-to-text transfer transformer (T5) and innovatively reducing its out-of-class predictions. The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes & struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split. Our model achieves an F1 score of 0.5452 on the official test set.
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Co-authors
- Tosin Adewumi 2
- Marcus Liwicki 2
- Hamam Mokayed 1
- Foteini Liwicki 1
- Isabella Södergren 1
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