M asakha POS : Part-of-Speech Tagging for Typologically Diverse A frican languages (2023)

Abstract: In this paper, we present AfricaPOS, 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 universal dependencies (UD) guidelines. We conducted extensive POS baseline experiments using both conditional random field and several multilingual pre-trained language models. We applied various cross-lingual transfer models trained with data available in the UD. Evaluating on the AfricaPOS 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 parameter-fine-tuning methods. Crucially, transferring knowledge from a language that matches the language family and morphosyntactic properties seems to be more effective for POS tagging in unseen languages.

## introduction part-of-speech (pos) tagging is a process of assigning the most probable grammatical category * equal contribution. there have been recent works on the development of benchmark datasets for training and evaluating models in african languages for various nlp tasks, including machine translation @xcite ), text-tospeech @xcite , speech recognition @xcite , sentiment analysis @xcite , news topic classification @xcite , and named entity recognition @xcite . however, there is no large-scale dataset for pos covering several african languages. to tackle the data bottleneck issue for lowresource languages, recent work applied crosslingual transfer @xcite @xcite using multilingual pretrained language models (plms) @xcite to model specific phenomena in low-resource target languages. while such a cross-lingual transfer is often evaluated by fine-tuning multilingual models on english data, more recent work has shown that english is not often the best transfer language @xcite @xcite . contributions in this paper, we develop masakhapos -the largest pos dataset for 20 typologically diverse african languages. we highlight the challenges of annotating pos for these diverse languages using the universal dependencies (ud) @xcite guidelines such as tokenization issues, and pos tags ambiguities. we provide extensive pos baselines using conditional random field (crf) and several multilingual pretrained language models (plms). furthermore, we experimented with different parameter-efficient cross-lingual transfer methods @xcite , and transfer languages with available training data in the ud. our evaluation demonstrates that choosing the best transfer language(s) in both single-source and multi-source setups leads to large improvements in pos tagging performance, especially when combined with parameter-fine-tuning methods. finally, we show that a transfer language that belongs to the same language family and shares similar morphological characteristics (e.g. non-bantu niger-congo) seems to be more effective for tagging pos in unseen languages. for reproducibility, we release our code, data and models on github foot_0 2 related work in the past, efforts have been made to build a pos tagger for several african languages, including hausa @xcite , igbo @xcite , kinyarwanda @xcite , luo @xcite , setswana @xcite , isixhosa @xcite , @xcite , @xcite , and isizulu @xcite . while pos tagging has been investigated for the aforementioned languages, annotated datasets exist only in a few african languages. in the universal dependencies dataset @xcite , nine african languages foot_1 are represented. still, only four of the nine languages have training data, i.e. afrikaans, coptic, nigerian-pidgin, and wolof. in this work, we create the largest pos dataset for 20 african languages following the ud annotation guidelines. ## languages and their characteristics we focus on 20 sub-saharan african languages, spoken in circa 27 countries in the western, eastern, central and southern regions of africa. an overview of the focus languages is provided in the writing system of our focus languages is mostly based on latin script (sometimes with additional letters and diacritics). besides naija, kiswahili, and wolof, the remaining languages are all tonal. as far as morphosyntax is concerned, noun classification is a prominent grammatical feature for an important part of our focus languages. 12 of the languages actively make use of between 6-20 noun classes. this includes all bantu languages, ghomálá', mossi, akan and wolof @xcite @xcite . noun classes can play a central role in pos annotation. for instance, in isixhosa, adding the class prefix can change the grammatical category of the word @xcite . all languages use the svo word order, while bambara additionally uses the sov word order. appendix a provides the details about the language characteristics. ## annotation challenges when annotating our focus languages, we faced two main challenges: tokenization and pos ambiguities. ## conclusion in this paper, we created masakhapos, the largest pos dataset for 20 typologically-diverse african languages. we showed that pos annotation of these languages based on the ud scheme can be quite challenging, especially with regard to word segmentation and pos ambiguities. we provide pos baseline models using crf and by fine-tuning multilingual plms. we analyze cross-lingual transfer on masakhapos dataset in single-source and multi-source settings. an important finding that emerged from this study is that choosing the appropriate transfer languages substantially improves pos tagging for unseen languages. the transfer performance is particularly effective when pretraining includes a language that shares typological features with the target languages. news domain our annotated dataset belong to the news domain, which is a popular domain in ud. however, the pos dataset and models may not generalize to other domains like speech transcript, conversation data etc. transfer results may not generalize to all nlp tasks we have only experimented with pos task, the best transfer language e.g for non-bantu niger-congo languages i.e wolof, may not be the same for other nlp tasks.