Papers by Kushal Tatariya
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models (2024.emnlp-main)
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| Challenge: | PIXEL is a vision transformer that has been pre-trained on rendered text . however, it is not able to outperform monolingual subwords like BERT . |
| Approach: | They propose to use PIXEL as a vision transformer to train on rendered text to explore the gap between its visual and linguistic understanding. |
| Outcome: | The proposed model outperforms monolingual subword models in most other contexts, but it lacks the linguistic knowledge to perform in language tasks. |
How Good is Your Wikipedia? Auditing Data Quality for Low-resource and Multilingual NLP (2026.acl-long)
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Kushal Tatariya, Artur Kulmizev, Wessel Poelman, Esther Ploeger, Marcel Bollmann, Johannes Bjerva, Jiaming Luo, Heather Lent, Miryam de Lhoneux
| Challenge: | Wikipedia’s perceived high quality and broad language coverage have established it as a fundamental resource in NLP. |
| Approach: | They propose a data filtering procedure which removes a large percentage of Wikipedia's data and a 4-level quality ranking of the site. |
| Outcome: | The results show that the proposed filtering procedure outperforms the raw Wikipedia models in three language modelling scenarios. |