MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages

التفاصيل البيبلوغرافية
العنوان: MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages
المؤلفون: Dione, Cheikh M. Bamba, Adelani, David, Nabende, Peter, Alabi, Jesujoba, Sindane, Thapelo, Buzaaba, Happy, Muhammad, Shamsuddeen Hassan, Emezue, Chris Chinenye, Ogayo, Perez, Aremu, Anuoluwapo, Gitau, Catherine, Mbaye, Derguene, Mukiibi, Jonathan, Sibanda, Blessing, Dossou, Bonaventure F. P., Bukula, Andiswa, Mabuya, Rooweither, Tapo, Allahsera Auguste, Munkoh-Buabeng, Edwin, Koagne, victoire Memdjokam, Kabore, Fatoumata Ouoba, Taylor, Amelia, Kalipe, Godson, Macucwa, Tebogo, Marivate, Vukosi, Gwadabe, Tajuddeen, Elvis, Mboning Tchiaze, Onyenwe, Ikechukwu, Atindogbe, Gratien, Adelani, Tolulope, Akinade, Idris, Samuel, Olanrewaju, Nahimana, Marien, Musabeyezu, Théogène, Niyomutabazi, Emile, Chimhenga, Ester, Gotosa, Kudzai, Mizha, Patrick, Agbolo, Apelete, Traore, Seydou, Uchechukwu, Chinedu, Yusuf, Aliyu, Abdullahi, Muhammad, Klakow, Dietrich
سنة النشر: 2023
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computation and Language
الوصف: In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the UD (universal dependencies) guidelines. We conducted extensive POS baseline experiments using conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in UD. Evaluating on the MasakhaPOS dataset, we show that choosing the best transfer language(s) in both single-source and multi-source setups greatly improves the POS tagging performance of the target languages, in particular when combined with cross-lingual parameter-efficient fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems more effective for POS tagging in unseen languages.
Comment: Accepted to ACL 2023 (Main conference)
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2305.13989
رقم الأكسشن: edsarx.2305.13989
قاعدة البيانات: arXiv