Papers by Christopher M Homan
How Many Ratings per Item are Necessary for Reliable Significance Testing? (2026.findings-eacl)
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| Challenge: | Existing methods for estimating model reliability are based on a few output responses per item. |
| Approach: | They propose a method to determine whether an existing dataset has enough responses per item to assure reliable null hypothesis statistical testing. |
| Outcome: | The proposed method can help researchers make better decisions about how to collect data for AI evaluation. |
NSL-MT: Linguistically Informed Negative Samples for Efficient Machine Translation in African Low-Resource Languages (2026.findings-acl)
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| Challenge: | In low-resource settings, models encounter too few examples to reliably distinguish grammatical patterns from noise. |
| Approach: | They propose a negative space learning machine translation (NSL-MT) method that augments limited parallel data with synthetically generated violations of the target language’s grammar and explicitly penalizes the model when it assigns high probability to these violations. |
| Outcome: | The proposed method delivers 3-12% BLEU gains for well-performing models and 56-89% gains for models lacking decent initial support. |
Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala (2025.naacl-srw)
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Shanilka Haturusinghe, Tharindu Cyril Weerasooriya, Christopher M Homan, Marcos Zampieri, Sidath Ravindra Liyanage
| Challenge: | A major challenge in the field of NLP are the disparities between high- and low-resource languages. |
| Approach: | They propose fine-tuning strategies that have not been previously explored for Sinhala in the downstream task of offensive language detection. |
| Outcome: | The proposed models outperform baseline models on the Sinhala offensive language detection task. |
GAIfE: Using GenAI to Improve Literacy in Low-resourced Settings (2025.findings-naacl)
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Allahsera Auguste Tapo, Nouhoum Coulibaly, Seydou Diallo, Sebastien Diarra, Christopher M Homan, Mamadou K. Keita, Michael Leventhal
| Challenge: | Illiteracy is a predictor of many negative social and personal outcomes in underresourced countries, where few books exist that are suitable for children to learn to read from. |
| Approach: | They propose to use generative AI to create culturally-engaging materials for learning in mali's vehicular language Bambara by multiplying the content by 10 times . authors propose to apply bias-aware tools to reduce illiteracy and improve learning outcomes through native language education. |
| Outcome: | The proposed toolchain and workflow can be adapted to address low literacy in mali using generative AI. |
Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech (2025.emnlp-main)
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| Challenge: | a new study examines how users interact with LGBTQ+ news content . a corpus of 1,419,047 comments on 3,161 YouTube news videos is used to analyze the content - both positive and negative - of cable news outlets. |
| Approach: | They analyze how users interact with LGBTQ+ news content via a corpus of 1,419,047 comments on 3,161 YouTube news videos of major US cable news outlets. |
| Outcome: | The proposed classifier detects positive (hope speech), negative, neutral, and irrelevant content. |
Bayelemabaga: Creating Resources for Bambara NLP (2025.naacl-long)
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| Challenge: | a lack of well-structured multilingual datasets remains a challenge for machine translation in under-resource languages. |
| Approach: | They propose to create a multilingual dataset for machine translation in the Bambara language, the vehicular language of Mali. |
| Outcome: | The proposed dataset is the most extensive curated multilingual dataset for machine translation in the Bambara language, the vehicular language of Mali. |