Papers by Iyanuoluwa Shode
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification (2023.acl-short)
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| Challenge: | Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. |
| Approach: | They propose to use a dataset to classify sentiments for cross-domain adaptation for Nigerian and other African languages. |
| Outcome: | The proposed dataset compares the performance of cross-domain adaptation from Twitter domain and cross-lingual adaptation from English domain. |
Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs (2025.findings-naacl)
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| Challenge: | Nigeria is a multilingual country with 500+ languages. |
| Approach: | They propose to use a pidgin and a creole to analyze the pidgins of Nigeria . they also use machine translation to analyze their results . |
| Outcome: | The results show that the two pidgins do not represent each other and are hard to teach . the results show the pidgin varieties are underrepresented in Generative AI . |
FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms (2023.starsem-1)
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Patrick Lee, Iyanuoluwa Shode, Alain Trujillo, Yuan Zhao, Olumide Ojo, Diana Plancarte, Anna Feldman, Jing Peng
| Challenge: | Existing work on euphemism disambiguation tasks has focused on transformers . euphorias are expressions that soften the message they convey, therefore dictionary-based approaches are ineffective . |
| Approach: | They propose to annotate PETs for vagueness and use transformers to classify PETs . they perform euphemism disambiguation experiments in three different languages . |
| Outcome: | The proposed models perform well in English euphemism disambiguation task . preliminary results will be used to launch future work . |
MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms (2024.findings-eacl)
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Patrick Lee, Alain Chirino Trujillo, Diana Cuevas Plancarte, Olumide Ojo, Xinyi Liu, Iyanuoluwa Shode, Yuan Zhao, Anna Feldman, Jing Peng
| Challenge: | Euphemisms are a linguistic device used to soften or neutralize language that may otherwise be harsh or awkward to state directly. |
| Approach: | They train a multilingual transformer model to disambiguate potentially euphemistic terms in multilingual and cross-lingual settings. |
| Outcome: | The proposed model performs better than monolingual models on the disambiguation task compared to monolingual ones in multilingual and cross-lingual settings. |
Cross-lingual Open-Retrieval Question Answering for African Languages (2023.findings-emnlp)
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Odunayo Ogundepo, Tajuddeen Gwadabe, Clara Rivera, Jonathan Clark, Sebastian Ruder, David Adelani, Bonaventure Dossou, Abdou Diop, Claytone Sikasote, Gilles Hacheme, Happy Buzaaba, Ignatius Ezeani, Rooweither Mabuya, Salomey Osei, Chris Emezue, Albert Kahira, Shamsuddeen Muhammad, Akintunde Oladipo, Abraham Owodunni, Atnafu Tonja, Iyanuoluwa Shode, Akari Asai, Anuoluwapo Aremu, Ayodele Awokoya, Bernard Opoku, Chiamaka Chukwuneke, Christine Mwase, Clemencia Siro, Stephen Arthur, Tunde Ajayi, Verrah Otiende, Andre Rubungo, Boyd Sinkala, Daniel Ajisafe, Emeka Onwuegbuzia, Falalu Lawan, Ibrahim Ahmad, Jesujoba Alabi, Chinedu Mbonu, Mofetoluwa Adeyemi, Mofya Phiri, Orevaoghene Ahia, Ruqayya Iro, Sonia Adhiambo
| Challenge: | Our Dataset is the first cross-lingual QA dataset with a focus on African languages. |
| Approach: | They propose to use African languages as the only high-coverage source of answer content for cross-lingual open-retrieval question answering systems. |
| Outcome: | Our Dataset includes 12,000+ XOR QA examples across 10 African languages. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |