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Your search keyword '"Mohamed, Shafie Abdi"' showing total 4 results
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4 results on '"Mohamed, Shafie Abdi"'

1. AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages

Author

Wang, Jiayi, Adelani, David Ifeoluwa, Agrawal, Sweta, Masiak, Marek, Rei, Ricardo, Briakou, Eleftheria, Carpuat, Marine, He, Xuanli, Bourhim, Sofia, Bukula, Andiswa, Mohamed, Muhidin, Olatoye, Temitayo, Adewumi, Tosin, Mokayed, Hamam, Mwase, Christine, Kimotho, Wangui, Yuehgoh, Foutse, Aremu, Anuoluwapo, Ojo, Jessica, Muhammad, Shamsuddeen Hassan, Osei, Salomey, Omotayo, Abdul-Hakeem, Chukwuneke, Chiamaka, Ogayo, Perez, Hourrane, Oumaima, Anigri, Salma El, Ndolela, Lolwethu, Mangwana, Thabiso, Mohamed, Shafie Abdi, Hassan, Ayinde, Awoyomi, Oluwabusayo Olufunke, Alkhaled, Lama, Al-Azzawi, Sana, Etori, Naome A., Ochieng, Millicent, Siro, Clemencia, Njoroge, Samuel, Muchiri, Eric, Kimotho, Wangari, Momo, Lyse Naomi Wamba, Abolade, Daud, Ajao, Simbiat, Shode, Iyanuoluwa, Macharm, Ricky, Iro, Ruqayya Nasir, Abdullahi, Saheed S., Moore, Stephen E., Opoku, Bernard, Akinjobi, Zainab, Afolabi, Abeeb, Obiefuna, Nnaemeka, Ogbu, Onyekachi Raphael, Brian, Sam, Otiende, Verrah Akinyi, Mbonu, Chinedu Emmanuel, Sari, Sakayo Toadoum, Lu, Yao, and Stenetorp, Pontus

Subjects
Computer Science - Computation and Language
Abstract

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441)., Comment: Accepted by NAACL 2024

Published
2023

2. Lexicon and Rule-based Word Lemmatization Approach for the Somali Language

Author

Mohamed, Shafie Abdi and Mohamed, Muhidin Abdullahi

Subjects
Computer Science - Computation and Language
Abstract

Lemmatization is a Natural Language Processing (NLP) technique used to normalize text by changing morphological derivations of words to their root forms. It is used as a core pre-processing step in many NLP tasks including text indexing, information retrieval, and machine learning for NLP, among others. This paper pioneers the development of text lemmatization for the Somali language, a low-resource language with very limited or no prior effective adoption of NLP methods and datasets. We especially develop a lexicon and rule-based lemmatizer for Somali text, which is a starting point for a full-fledged Somali lemmatization system for various NLP tasks. With consideration of the language morphological rules, we have developed an initial lexicon of 1247 root words and 7173 derivationally related terms enriched with rules for lemmatizing words not present in the lexicon. We have tested the algorithm on 120 documents of various lengths including news articles, social media posts, and text messages. Our initial results demonstrate that the algorithm achieves an accuracy of 57\% for relatively long documents (e.g. full news articles), 60.57\% for news article extracts, and high accuracy of 95.87\% for short texts such as social media messages.

Published
2023

3. MasakhaNEWS:News Topic Classification for African languages

Author

Adelani, David Ifeoluwa, Chukwuneke, Chiamaka I., Masiak, Marek, Azime, Israel Abebe, Alabi, Jesujoba Oluwadara, Tonja, Atnafu Lambebo, Mwase, Christine, Ogundepo, Odunayo, Dossou, Bonaventure F. P., Oladipo, Akintunde, Nixdorf, Doreen, Emezue, Chris Chinenye, al-azzawi, Sana Sabah, Sibanda, Blessing K., David, Davis, Ndolela, Lolwethu, Mukiibi, Jonathan, Ajayi, Tunde Oluwaseyi, Ngoli, Tatiana Moteu, Odhiambo, Brian, Mbonu, Chinedu E., Owodunni, Abraham Toluwase, Obiefuna, Nnaemeka C., Muhammad, Shamsuddeen Hassan, Abdullahi, Saheed Salahudeen, Yigezu, Mesay Gemeda, Gwadabe, Tajuddeen, Abdulmumin, Idris, Bame, Mahlet Taye, Awoyomi, Oluwabusayo Olufunke, Shode, Iyanuoluwa, Adelani, Tolulope Anu, Kailani, Habiba Abdulganiy, Omotayo, Abdul-Hakeem, Adeeko, Adetola, Abeeb, Afolabi, Aremu, Anuoluwapo, Samuel, Olanrewaju, Siro, Clemencia, Kimotho, Wangari, Ogbu, Onyekachi Raphael, Fanijo, Samuel, Ojo, Jessica, Awosan, Oyinkansola F., Guge, Tadesse Kebede, Sari, Sakayo Toadoum, Nyatsine, Pamela, Sidume, Freedmore, Yousuf, Oreen, Oduwole, Mardiyyah, Kimanuka, Ussen, Tshinu, Kanda Patrick, Diko, Thina, Nxakama, Siyanda, Johar, Abdulmejid Tuni, Gebre, Sinodos, Mohamed, Muhidin, Mohamed, Shafie Abdi, Hassan, Fuad Mire, Mehamed, Moges Ahmed, Ngabire, Evrard, Stenetorp, Pontus, Adelani, David Ifeoluwa, Chukwuneke, Chiamaka I., Masiak, Marek, Azime, Israel Abebe, Alabi, Jesujoba Oluwadara, Tonja, Atnafu Lambebo, Mwase, Christine, Ogundepo, Odunayo, Dossou, Bonaventure F. P., Oladipo, Akintunde, Nixdorf, Doreen, Emezue, Chris Chinenye, al-azzawi, Sana Sabah, Sibanda, Blessing K., David, Davis, Ndolela, Lolwethu, Mukiibi, Jonathan, Ajayi, Tunde Oluwaseyi, Ngoli, Tatiana Moteu, Odhiambo, Brian, Mbonu, Chinedu E., Owodunni, Abraham Toluwase, Obiefuna, Nnaemeka C., Muhammad, Shamsuddeen Hassan, Abdullahi, Saheed Salahudeen, Yigezu, Mesay Gemeda, Gwadabe, Tajuddeen, Abdulmumin, Idris, Bame, Mahlet Taye, Awoyomi, Oluwabusayo Olufunke, Shode, Iyanuoluwa, Adelani, Tolulope Anu, Kailani, Habiba Abdulganiy, Omotayo, Abdul-Hakeem, Adeeko, Adetola, Abeeb, Afolabi, Aremu, Anuoluwapo, Samuel, Olanrewaju, Siro, Clemencia, Kimotho, Wangari, Ogbu, Onyekachi Raphael, Fanijo, Samuel, Ojo, Jessica, Awosan, Oyinkansola F., Guge, Tadesse Kebede, Sari, Sakayo Toadoum, Nyatsine, Pamela, Sidume, Freedmore, Yousuf, Oreen, Oduwole, Mardiyyah, Kimanuka, Ussen, Tshinu, Kanda Patrick, Diko, Thina, Nxakama, Siyanda, Johar, Abdulmejid Tuni, Gebre, Sinodos, Mohamed, Muhidin, Mohamed, Shafie Abdi, Hassan, Fuad Mire, Mehamed, Moges Ahmed, Ngabire, Evrard, and Stenetorp, Pontus

Abstract

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach.

Published
2023

4. MasakhaNEWS: News Topic Classification for African languages

Author

Adelani, David Ifeoluwa, Masiak, Marek, Azime, Israel Abebe, Alabi, Jesujoba Oluwadara, Tonja, Atnafu Lambebo, Mwase, Christine, Ogundepo, Odunayo, Dossou, Bonaventure F. P., Oladipo, Akintunde, Nixdorf, Doreen, Emezue, Chris Chinenye, al-azzawi, Sana Sabah, Sibanda, Blessing K., David, Davis, Ndolela, Lolwethu, Mukiibi, Jonathan, Ajayi, Tunde Oluwaseyi, Ngoli, Tatiana Moteu, Odhiambo, Brian, Owodunni, Abraham Toluwase, Obiefuna, Nnaemeka C., Mohamed, Muhidin, Muhammad, Shamsuddeen Hassan, Ababu, Teshome Mulugeta, Abdullahi, Saheed Salahudeen, Yigezu, Mesay Gemeda, Gwadabe, Tajuddeen, Abdulmumin, Idris, Bame, Mahlet Taye, Awoyomi, Oluwabusayo Olufunke, Shode, Iyanuoluwa, Adelani, Tolulope Anu, Kailani, Habiba Abdulganiy, Omotayo, Abdul-Hakeem, Adeeko, Adetola, Abeeb, Afolabi, Aremu, Anuoluwapo, Samuel, Olanrewaju, Siro, Clemencia, Kimotho, Wangari, Ogbu, Onyekachi Raphael, Mbonu, Chinedu E., Chukwuneke, Chiamaka I., Fanijo, Samuel, Ojo, Jessica, Awosan, Oyinkansola F., Guge, Tadesse Kebede, Sari, Sakayo Toadoum, Nyatsine, Pamela, Sidume, Freedmore, Yousuf, Oreen, Oduwole, Mardiyyah, Tshinu, Kanda Patrick, Kimanuka, Ussen, Diko, Thina, Nxakama, Siyanda, Nugussie, Sinodos G., Johar, Abdulmejid Tuni, Mohamed, Shafie Abdi, Hassan, Fuad Mire, Mehamed, Moges Ahmed, Ngabire, Evrard, Twagirayezu, Jules, Ssenkungu, Ivan, Stenetorp, Pontus, Adelani, David Ifeoluwa, Masiak, Marek, Azime, Israel Abebe, Alabi, Jesujoba Oluwadara, Tonja, Atnafu Lambebo, Mwase, Christine, Ogundepo, Odunayo, Dossou, Bonaventure F. P., Oladipo, Akintunde, Nixdorf, Doreen, Emezue, Chris Chinenye, al-azzawi, Sana Sabah, Sibanda, Blessing K., David, Davis, Ndolela, Lolwethu, Mukiibi, Jonathan, Ajayi, Tunde Oluwaseyi, Ngoli, Tatiana Moteu, Odhiambo, Brian, Owodunni, Abraham Toluwase, Obiefuna, Nnaemeka C., Mohamed, Muhidin, Muhammad, Shamsuddeen Hassan, Ababu, Teshome Mulugeta, Abdullahi, Saheed Salahudeen, Yigezu, Mesay Gemeda, Gwadabe, Tajuddeen, Abdulmumin, Idris, Bame, Mahlet Taye, Awoyomi, Oluwabusayo Olufunke, Shode, Iyanuoluwa, Adelani, Tolulope Anu, Kailani, Habiba Abdulganiy, Omotayo, Abdul-Hakeem, Adeeko, Adetola, Abeeb, Afolabi, Aremu, Anuoluwapo, Samuel, Olanrewaju, Siro, Clemencia, Kimotho, Wangari, Ogbu, Onyekachi Raphael, Mbonu, Chinedu E., Chukwuneke, Chiamaka I., Fanijo, Samuel, Ojo, Jessica, Awosan, Oyinkansola F., Guge, Tadesse Kebede, Sari, Sakayo Toadoum, Nyatsine, Pamela, Sidume, Freedmore, Yousuf, Oreen, Oduwole, Mardiyyah, Tshinu, Kanda Patrick, Kimanuka, Ussen, Diko, Thina, Nxakama, Siyanda, Nugussie, Sinodos G., Johar, Abdulmejid Tuni, Mohamed, Shafie Abdi, Hassan, Fuad Mire, Mehamed, Moges Ahmed, Ngabire, Evrard, Twagirayezu, Jules, Ssenkungu, Ivan, and Stenetorp, Pontus

Abstract

African languages are severely under-represented in NLP research due to lack of datasets covering several NLP tasks. While there are individual language specific datasets that are being expanded to different tasks, only a handful of NLP tasks (e.g. named entity recognition and machine translation) have standardized benchmark datasets covering several geographical and typologically-diverse African languages. In this paper, we develop MasakhaNEWS -- a new benchmark dataset for news topic classification covering 16 languages widely spoken in Africa. We provide an evaluation of baseline models by training classical machine learning models and fine-tuning several language models. Furthermore, we explore several alternatives to full fine-tuning of language models that are better suited for zero-shot and few-shot learning such as cross-lingual parameter-efficient fine-tuning (like MAD-X), pattern exploiting training (PET), prompting language models (like ChatGPT), and prompt-free sentence transformer fine-tuning (SetFit and Cohere Embedding API). Our evaluation in zero-shot setting shows the potential of prompting ChatGPT for news topic classification in low-resource African languages, achieving an average performance of 70 F1 points without leveraging additional supervision like MAD-X. In few-shot setting, we show that with as little as 10 examples per label, we achieved more than 90\% (i.e. 86.0 F1 points) of the performance of full supervised training (92.6 F1 points) leveraging the PET approach.

Published
2023

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