Ife Adebara


Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go
Ife Adebara | Muhammad Abdul-Mageed
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Aligning with ACL 2022 special Theme on “Language Diversity: from Low Resource to Endangered Languages”, we discuss the major linguistic and sociopolitical challenges facing development of NLP technologies for African languages. Situating African languages in a typological framework, we discuss how the particulars of these languages can be harnessed. To facilitate future research, we also highlight current efforts, communities, venues, datasets, and tools. Our main objective is to motivate and advocate for an Afrocentric approach to technology development. With this in mind, we recommend what technologies to build and how to build, evaluate, and deploy them based on the needs of local African communities.

AfroLID: A Neural Language Identification Tool for African Languages
Ife Adebara | AbdelRahim Elmadany | Muhammad Abdul-Mageed | Alcides Inciarte
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world’s 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for 517 African languages and varieties. AfroLID exploits a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems. When evaluated on our blind Test set, AfroLID achieves 95.89 F_1-score. We also compare AfroLID to five existing LID tools that each cover a small number of African languages, finding it to outperform them on most languages. We further show the utility of AfroLID in the wild by testing it on the acutely under-served Twitter domain. Finally, we offer a number of controlled case studies and perform a linguistically-motivated error analysis that allow us to both showcase AfroLID’s powerful capabilities and limitations

Linguistically-Motivated Yorùbá-English Machine Translation
Ife Adebara | Muhammad Abdul-Mageed | Miikka Silfverberg
Proceedings of the 29th International Conference on Computational Linguistics

Translating between languages where certain features are marked morphologically in one but absent or marked contextually in the other is an important test case for machine translation. When translating into English which marks (in)definiteness morphologically, from Yorùbá which uses bare nouns but marks these features contextually, ambiguities arise. In this work, we perform fine-grained analysis on how an SMT system compares with two NMT systems (BiLSTM and Transformer) when translating bare nouns in Yorùbá into English. We investigate how the systems what extent they identify BNs, correctly translate them, and compare with human translation patterns. We also analyze the type of errors each model makes and provide a linguistic description of these errors. We glean insights for evaluating model performance in low-resource settings. In translating bare nouns, our results show the transformer model outperforms the SMT and BiLSTM models for 4 categories, the BiLSTM outperforms the SMT model for 3 categories while the SMT outperforms the NMT models for 1 category.


Improving Similar Language Translation With Transfer Learning
Ife Adebara | Muhammad Abdul-Mageed
Proceedings of the Sixth Conference on Machine Translation

We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU)and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.


Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformers
Ife Adebara | El Moatez Billah Nagoudi | Muhammad Abdul Mageed
Proceedings of the Fifth Conference on Machine Translation

In this work we investigate different approaches to translate between similar languages despite low resource limitations. This work is done as the participation of the UBC NLP research group in the WMT 2019 Similar Languages Translation Shared Task. We participated in all language pairs and performed various experiments. We used a transformer architecture for all the models and used back-translation for one of the language pairs. We explore both bilingual and multi-lingual approaches. We describe the pre-processing, training, translation and results for each model. We also investigate the role of mutual intelligibility in model performance.