Papers by Iffat Maab
AFRIDOC-MT: Document-level MT Corpus for African Languages (2025.emnlp-main)
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Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, Dietrich Klakow
| Challenge: | AFRIDOC-MT is a document-level multi-parallel translation dataset covering five languages . AFRITIC-MT models perform better on sentences than general-purpose LLMs . |
| Approach: | They propose a document-level multi-parallel translation dataset covering English and five African languages. |
| Outcome: | The proposed dataset covers 334 health and 271 information technology news documents . it shows that NLLB-200 achieves the best average performance among standard models . |
Media Bias Detection Across Families of Language Models (2024.naacl-long)
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| Challenge: | Traditional NLP models have shown good performance in classifying media bias, but require careful model design and extensive tuning. |
| Approach: | They ask how well prompting of large language models can recognize media bias. |
| Outcome: | The prompt-based models deliver comparable performance to traditional models with greatly reduced effort and the availability of context substantially improves results. |
When Bigger Isn’t Better: A Comprehensive Fairness Evaluation of Political Bias in Multi-News Summarisation (2026.acl-long)
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| Challenge: | Existing models that deal with multiple sources can exhibit political biases, causing unequal representation of viewpoints and underrepresentation of minority voices. |
| Approach: | They examine how large language models handle sources with varying political leanings using a dataset with political orientation labels. |
| Outcome: | The proposed model outperforms larger models and offers the best balance of fairness and efficiency. |
Prompt-driven Detection of Offensive Urdu Language using Large Language Models (2026.eacl-long)
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| Challenge: | Offensive language detection systems require extensive tuning and careful model design . a resource gap exists for addressing offensive languages, especially those transcribed in non-native scripts . |
| Approach: | They evaluate pre-trained LLMs using different transcriptions of the Urdu language to assess their performance . they find that they can detect hateful and offensive content in diverse linguistic contexts . |
| Outcome: | The proposed methods perform comparable to fine-tuned benchmarks in diverse languages. |
Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are increasingly being used to understand how scientific research evolves, drawing growing interest from the research community. |
| Approach: | They propose a scientific fact-checking dataset, SCINLP, tailored to the NLP domain that verifies the veracity of scientific research questions across varying rationale contexts. |
| Outcome: | The proposed framework examines scientific claims and research focus from a curated collection of influential and reputable NLP papers published between 2000 and 2024. |