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

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 10883–10900 July 9-14, 2023 ©2023 Association for Computational Linguistics MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages Cheikh M. Bamba Dione1,†,∗, David Ifeoluwa Adelani2,†,∗, Peter Nabende3,†, Jesujoba O. Alabi4,†, Thapelo Sindane5, Happy Buzaaba6†, Shamsuddeen Hassan Muhammad7,8†, Chris Chinenye Emezue9,10†, Perez Ogayo11†, Anuoluwapo Aremu†, Catherine Gitau†, Derguene Mbaye12†, Jonathan Mukiibi3†, Blessing Sibanda†, Bonaventure F. P. Dossou10,13,14†, Andiswa Bukula15, Rooweither Mabuya15, Allahsera Auguste Tapo16†, Edwin Munkoh-Buabeng17†, Victoire Memdjokam Koagne†, Fatoumata Ouoba Kabore18†, Amelia Taylor19, Godson Kalipe†, Tebogo Macucwa5, Vukosi Marivate5,13†, Tajuddeen Gwadabe†, Elvis Tchiaze Mboning†, Ikechukwu Onyenwe20, Gratien Atindogbe21, Tolulope Anu Adelani†, Idris Akinade22, Olanrewaju Samuel†, Marien Nahimana, Théogène Musabeyezu, Emile Niyomutabazi, Ester Chimhenga, Kudzai Gotosa, Patrick Mizha, Apelete Agbolo23, Seydou Traore24, Chinedu Uchechukwu20, Aliyu Yusuf8, Muhammad Abdullahi8, Dietrich Klakow4 †Masakhane NLP, 1Université Gaston Berger, Senegal, 2University College London, UK, 3Makerere University, Uganda, 4Saarland University, Germany, 5University of Pretoria, South Africa, 6 RIKEN Center for AIP, Japan, 7Bayero University Kano, Nigeria. 8University of Porto, Portugal, 9Technical University of Munich, Germany, 10Lanfrica, 11Carnegie Mellon University, USA, 12Baamtu, Senegal, 13Lelapa AI, 14Mila Quebec AI Institute, Canada, 15SADiLaR, South Africa, 16Rochester Institute of Technology, USA, 17TU Clausthal, Germany, 18Uppsala University, Sweden, 19Malawi University of Business and Applied Science, Malawi, 20Nnamdi Azikiwe University, Nigeria, 21University of Buea, Cameroon, 22University of Ibadan, Nigeria, 23Ewegbe Akademi, Togo, 24AMALAN, Mali. Abstract In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 ty- pologically diverse African languages. We discuss the challenges in annotating POS for these languages using the UD (universal de- pendencies) guidelines. We conducted exten- sive POS baseline experiments using condi- tional 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 Masakha- POS 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. Cru- cially, transferring knowledge from a language that matches the language family and mor- phosyntactic properties seems more effective for POS tagging in unseen languages. 1 Introduction Part-of-Speech (POS) tagging is a process of as- signing the most probable grammatical category ∗Equal contribution. (or tag) to each word (or token) in a given sen- tence of a particular natural language. POS tagging is one of the fundamental steps for many natural language processing (NLP) applications, including machine translation, parsing, text chunking, spell and grammar checking. While great strides have been made for (major) Indo-European languages such as English, French and German, work on the African languages is quite scarce. The vast major- ity of African languages lack annotated datasets for training and evaluating basic NLP systems. There have been recent works on the develop- ment of benchmark datasets for training and eval- uating models in African languages for various NLP tasks, including machine translation (NLLB- Team et al., 2022; Adelani et al., 2022a), text-to- speech (Ogayo …

Content truncated to 500 words