Publications

Long-term exposure to biased content in literature or media can significantly influence people’s perceptions of reality,
leading to the development of implicit biases that are difficult
to detect and address (Gerbner 1998). In this study, we propose a novel method to analyze the differences in representation between two groups and use it examine the representation of African Americans and White Americans in books
between 1850 to 2000 with the Google Books dataset (Goldberg and Orwant 2013). By developing better tools to understand differences in representation, we aim to contribute to
the ongoing efforts to recognize and mitigate biases. To improve upon the more common phrase-based (men, women,
white, black, etc) methods to differentiate context (Tripodi
et al. 2019; Lucy, Tadimeti, and Bamman 2022), we propose
collecting a comprehensive list of historically significant figures and using their names to select relevant context. This
novel approach offers a more accurate and nuanced method
for detecting implicit biases through reducing the risk of selection bias. We create group representations for each decade
and analyze them in an aligned semantic space (Hamilton,
Leskovec, and Jurafsky 2016). We further support our results
by assessing the time-adjusted toxicity (Bassignana, Basile,
and Patti 2018) in the context for each group and identifying
the semantic axes (Lucy, Tadimeti, and Bamman 2022) that
exhibit the most significant differences between the groups
across decades. We support our method by showing that our
proposed method can capture known socio-political changes
accurately and our findings indicate that while the relative
number of African American names mentioned in books have
increased over time, the context surrounding them remains
more toxic than white Americans.

@article{kocyigit2023novel,
  title={A Novel Method for Analysing Racial Bias: Collection of Person Level References},
  author={Kocyigit, Muhammed Yusuf and Andy, Anietie and Wijaya, Derry},
  journal={arXiv preprint arXiv:2310.15847},
  year={2023}
}

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on
translation-based methods, which can introduce the translationese effect. This phenomenon results in translated
sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach
for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves
presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without
direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based
approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators
and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.

@inproceedings{kuwanto2024mitigating, title={Mitigating Translationese in Low-resource Languages: The Storyboard Approach}, author={Kuwanto, Garry and Urua, Eno-Abasi E and Amuok, Priscilla Amondi and Muhammad, Shamsuddeen Hassan and Aremu, Anuoluwapo and Otiende, Verrah and Nanyanga, Loice Emma and Nyoike, Teresiah W and Akpan, Aniefon D and Ab Udouboh, Nsima and others}, booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, pages={11349--11360}, year={2024} }