Papers by Charles Kemp
Simpson’s Paradox and the Accuracy-Fluency Tradeoff in Translation (2024.acl-short)
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| Challenge: | Existing studies suggest that accuracy and fluency should trade off against each other, and that capturing every detail of the source is difficult for human raters to distinguish. |
| Approach: | They propose to evaluate the relationship between accuracy and fluency at the segment level and to use probabilities to estimate probabilities. |
| Outcome: | The proposed model relies on human judgments of accuracy and fluency collected in prior work on translation quality estimation. |
Predicting Human Translation Difficulty Using Automatic Word Alignment (2023.findings-acl)
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| Challenge: | Translation difficulty is a problem when translators are required to resolve translation ambiguity from multiple possible translations. |
| Approach: | They use word alignments computed over large scale bilingual corpora to develop predictors of lexical translation difficulty. |
| Outcome: | The proposed method improves on a previous embedding-based approach and can contribute to a deeper understanding of cross-lingual differences and of causes of translation difficulty. |
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset (2025.acl-long)
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Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood O. Yekini, Jonas Kemp, Katherine A Heller, Jude Chidubem Omeke, Chidi Asuzu Md, Naome A Etori, Aïmérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael L. Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu
| Challenge: | Recent advances in large language models (LLMs) performance on medical multiplechoice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. |
| Approach: | They introduce AfriMed-QA, the first largescale Pan-African English multi-specialty medical Question-Answering (QA) dataset, with 15,000 questions sourced from over 60 medical schools across 16 countries. |
| Outcome: | The proposed model outperforms other models in the medical field and is compared with other models. |
Where the Cat Sat: A Multilingual Framework for Spatial Language Understanding (2026.acl-long)
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| Challenge: | Existing work exhibits biases toward English and prepositional marking . Existing models are limited in understanding spatial relations across typologically diverse languages . |
| Approach: | They propose a multilingual framework and benchmark for spatial language understanding . they decompose spatial relations into surface elements and semantic components . their results suggest surface parsing does not entail spatial understanding - they argue . |
| Outcome: | The proposed framework and benchmark decomposes spatial relations into surface elements and semantic components. |