Nigerian English adaptation, Pidgin, has evolved over the years through multi-language code switching, code mixing and linguistic adaptation. While Pidgin preserves many of the words in the normal English language corpus, both in spelling and pronunciation, the fundamental meaning of these words have changed significantly. For example,'ginger' is not a plant but an expression of motivation and 'tank' is not a container but an expression of gratitude. The implication is that the current approach of using direct English sentiment analysis of social media text from Nigeria is sub-optimal, as it will not be able to capture the semantic variation and contextual evolution in the contemporary meaning of these words. In practice, while many words in Nigerian Pidgin adaptation are the same as the standard English, the full English language based sentiment analysis models are not designed to capture the full intent of the Nigerian pidgin when used alone or code-mixed. By augmenting scarce human labelled code-changed text with ample synthetic code-reformatted text and meaning, we achieve significant improvements in sentiment scoring. Our research explores how to understand sentiment in an intrasentential code mixing and switching context where there has been significant word localization.This work presents a 300 VADER lexicon compatible Nigerian Pidgin sentiment tokens and their scores and a 14,000 gold standard Nigerian Pidgin tweets and their sentiments labels.
LIAAD - INESC TEC；Faculty of Sciences-University of Porto（波尔图大学理学院）；Saarland University（萨尔大学）；Google Research（谷歌研究院）；Bayero University（巴耶罗大学）；Ahmadu Bello University（艾哈迈杜·贝罗大学）；Microsoft Research India（微软研究院印度）；Technical University of Munich（慕尼黑工业大学）；Clear Global；Kaduna state University（卡杜纳州立大学）
Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria (Hausa, Igbo, Nigerian-Pidgin, and Yorùbá ) consisting of around 30,000 annotated tweets per language (and 14,000 for Nigerian-Pidgin), including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We …
Nigerian Pidgin is arguably the most widely spoken language in Nigeria. Variants of this language are also spoken across West and Central Africa, making it a very important language. This work aims to establish supervised and unsupervised neural machine translation (NMT) baselines between English and Nigerian Pidgin. We implement and compare NMT models with different tokenization methods, creating a solid foundation for future works.
Does Generative AI speak Nigerian-Pidgin?: Issues about Representativeness and Bias for Multilingualism in LLMs
Mila - Quebec AI Institute McGill University, Canada（魁北克AI研究所麦吉尔大学，加拿大）；LT3, IDLab, Universiteit Gent（LT3，IDLab，根特大学）；Bloomberg（彭博）；University of Trento（特伦托大学）
SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
University of Porto（波尔图大学）；University of Cambridge（剑桥大学）；Universität Hamburg（汉堡大学）；Bahir Dar University（巴赫达尔大学）；National Research Council Canada（加拿大国家研究委员会）；Uppsala University（乌普萨拉大学）；Google Research（谷歌研究院）；Bayero University Kano（贝罗大学卡诺分校）；Ahmadu Bello University, Zaria（扎里亚艾哈迈德·贝罗大学）；University College London（伦敦大学学院）
Most of the common applications of Named Entity Recognition (NER) is on English and other highly available languages. In this work, we present our findings on Named Entity Recognition for 5 Nigerian Languages (Nigerian English, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are considered low-resourced, and very little openly available Natural Language Processing work has been done in most of them. In this work, individual NER models were trained and metrics recorded for each of the languages. We also worked on a combined model that can handle Named Entity Recognition (NER) …
University of Copenhagen（哥本哈根大学）；Uppsala University（乌普萨拉大学）；KU Leuven（鲁汶大学）；Wuhan University of Science and Technology（武汉科技大学）
Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature. Creoles typically result from the fusion of a foreign language with multiple local languages, and what grammatical and lexical features are transferred to the creole is a complex process. While creoles are generally stable, the prominence of some features may be much stronger with certain demographics or in some linguistic situations. This paper makes several contributions: We collect existing corpora and release models for Haitian Creole, Nigerian Pidgin English, …
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
University of Porto（波尔图大学）；Bahir Dar University（巴赫达尔大学）；University of Cambridge（剑桥大学）；Cardiff University（卡迪夫大学）；Universität Hamburg（汉堡大学）；Uppsala University（乌普萨拉大学）；National Research Council Canada（加拿大国家研究委员会）；Google Research（谷歌研究院）；Hassan II University of Casablanca（卡萨布兰卡哈桑二世大学）；LIAAD - INESC TEC；Bayero University Kano（卡诺巴耶罗大学）；dLab；University of Deusto（德乌斯托大学）；Kaduna State University（卡杜纳州立大学）；Digital Umuganda；Wollo University（沃洛大学）；Jimma University（吉马大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；Accra Institute of Technology（阿克拉技术学院）；Ahmadu Bello University（艾哈迈杜·贝洛大学）；University College London（伦敦大学学院）；HausaNLP
Luleå University of Technology（吕勒奥理工大学）；Masakhane；CIS
Dialogue generation is an important NLP task fraught with many challenges. The challenges become more daunting for low-resource African languages. To enable the creation of dialogue agents for African languages, we contribute the first high-quality dialogue datasets for 6 African languages: Swahili, Wolof, Hausa, Nigerian Pidgin English, Kinyarwanda & Yorùbá. These datasets consist of 1,500 turns each, which we translate from a portion of the English multi-domain MultiWOZ dataset. Subsequently, we investigate & analyze the effectiveness of modelling through transfer learning by utilziing state…
