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

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

MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages

Entrepreneurial competencies and the performance of informal SMEs: the contingent role of business environment

This study examined entrepreneurial competencies as a viable pathway for improving the innovative performance of SMEs in Nigeria's informal sector and the contingent roles of the business environment. A survey research design was used to gather data from 296 entrepreneurs who operate informal SMEs in Nigeria. Based on the findings from the SEM-PLS multivariate analysis, the study concluded that entrepreneurial competencies, especially organising, conceptual, learning, strategic, opportunity and risk-taking competencies, are essential for achieving higher innovation performance. The study also reveals that entrepreneurial competencies are useful towards mitigating environmental pressures resulting from operational turbulence and erratic policy changes, as the firm drives towards improving innovation outputs. As such, the entrepreneurship environment is becoming more endogenous as entrepreneurs, through their entrepreneurial competencies, have started to gain control over it. This study contributes to the entrepreneurship literature by highlighting the most essential competencies alongside the relevant contingencies. By doing that, this study offers a practical guide on priority competence area that entrepreneurship stakeholders, including entrepreneurs and policymakers, should consider for investment