Low-resource African languages pose unique challenges for natural language processing (NLP) tasks, including natural language generation (NLG). In this paper, we develop Cheetah, a massively multilingual NLG language model for African languages. Cheetah supports 517 African languages and language varieties, allowing us to address the scarcity of NLG resources and provide a solution to foster linguistic diversity. We demonstrate the effectiveness of Cheetah through comprehensive evaluations across six generation downstream tasks. In five of the six tasks, Cheetah significantly outperforms other models, showcasing its remarkable performance for generating coherent and contextually appropriate text in a wide range of African languages. We additionally conduct a detailed human evaluation to delve deeper into the linguistic capabilities of Cheetah. The introduction of Cheetah has far-reaching benefits for linguistic diversity. By leveraging pretrained models and adapting them to specific languages, our approach facilitates the development of practical NLG applications for African communities. The findings of this study contribute to advancing NLP research in low-resource settings, enabling greater accessibility and inclusion for African languages in a rapidly expanding digital landscape. We publicly release our models for research.
Computing and Internet access are substantially growing markets in Southern Africa, which brings with it increasing demands for local content and tools in indigenous African languages. Since most of those languages are low-resourced, efforts have gone into the notion of bootstrapping tools for one African language from another. This paper provides an overview of these efforts for Niger-Congo B (`Bantu') languages. Bootstrapping grammars for geographically distant languages has been shown to still have positive outcomes for morphology and rules or grammar-based natural language generation. Boot…
Faced with a considerable lack of resources in African languages to carry out work in Natural Language Processing (NLP), Natural Language Understanding (NLU) and artificial intelligence, the research teams of NTeALan association has set itself the objective of building open-source platforms for the collaborative construction of lexicographic data in African languages. In this article, we present our first reports after 2 years of collaborative construction of lexicographic resources useful for African NLP tools.
West African Pidgin English is a language that is significantly spoken in West Africa, consisting of at least 75 million speakers. Nevertheless, proper machine translation systems and relevant NLP datasets for pidgin English are virtually absent. In this work, we develop techniques targeted at bridging the gap between Pidgin English and English in the context of natural language generation. %As a proof of concept, we explore the proposed techniques in the area of data-to-text generation. By building upon the previously released monolingual Pidgin English text and parallel English data-to-text …
We highlight an important frontier in algorithmic fairness: disparity in the quality of natural language processing algorithms when applied to language from authors of different social groups. For example, current systems sometimes analyze the language of females and minorities more poorly than they do of whites and males. We conduct an empirical analysis of racial disparity in language identification for tweets written in African-American English, and discuss implications of disparity in NLP.
Currently, natural language processing (NLP) models proliferate language discrimination leading to potentially harmful societal impacts as a result of biased outcomes. For example, part-of-speech taggers trained on Mainstream American English (MAE) produce non-interpretable results when applied to African American English (AAE) as a result of language features not seen during training. In this work, we incorporate a human-in-the-loop paradigm to gain a better understanding of AAE speakers' behavior and their language use, and highlight the need for dialectal language inclusivity so that native…
Language models are the foundation of current neural network-based models for natural language understanding and generation. However, research on the intrinsic performance of language models on African languages has been extremely limited, which is made more challenging by the lack of large or standardised training and evaluation sets that exist for English and other high-resource languages. In this paper, we evaluate the performance of open-vocabulary language models on low-resource South African languages, using byte-pair encoding to handle the rich morphology of these languages. We evaluate…
This paper relates work done during the DiLAF project. It consists in converting 5 bilingual African language-French dictionaries originally in Word format into XML following the LMF model. The languages processed are Bambara, Hausa, Kanuri, Tamajaq and Songhai-zarma, still considered as under-resourced languages concerning Natural Language Processing tools. Once converted, the dictionaries are available online on the Jibiki platform for lookup and modification. The DiLAF project is first presented. A description of each dictionary follows. Then, the conversion methodology from .doc format to …
Virtual assistants and text chatbots have recently been gaining popularity. Given the short message nature of text-based chat interactions, the language identification systems of these bots might only have 15 or 20 characters to make a prediction. However, accurate text language identification is important, especially in the early stages of many multilingual natural language processing pipelines. This paper investigates the use of a naive Bayes classifier, to accurately predict the language family that a piece of text belongs to, combined with a lexicon based classifier to distinguish the spec…
Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead
Saarland University, Saarland Informatics Campus（萨尔兰大学，萨尔兰信息学校区）；LMU Munich and Munich Center for Machine Learning（慕尼黑大学及慕尼黑机器学习中心）；Mila - Quebec AI Institute, McGill University & Canada CIFAR AI Chair（魁北克人工智能研究所，麦吉尔大学及加拿大 CIFAR 人工智能 chair）
Data Caricatures: On the Representation of African American Language in Pretraining Corpora
Columbia University, Department of Computer Science（哥伦比亚大学计算机科学系）；University of Michigan, Department of Linguistics（密歇根大学语言学系）；University of Pennsylvania, School of Social Policy and Practice, Annenberg School for Communications（宾夕法尼亚大学社会政策与实践学院、安纳伯格通讯学院）；University of Tennessee, Knoxville, Department of English（田纳西大学基洛纳分校英语系）
Correcting FLORES Evaluation Dataset for Four African Languages
University of Pretoria（比勒陀利亚大学）；Council for Scientific and Industrial Research（科学与工业研究理事会）；Imperial College（帝国理工学院）；Northeastern University（东北大学）；University of KwaZulu-Natal（夸祖鲁-纳塔尔大学）；Lelapa AI（莱拉帕人工智能公司）
Technical University of Munich（慕尼黑工业大学）；Jacobs University（雅各布斯大学）
Over the years, there have been campaigns to include the African languages in the growing research on machine translation (MT) in particular, and natural language processing (NLP) in general. Africa has the highest language diversity, with 1500-2000 documented languages and many more undocumented or extinct languages(Lewis, 2009; Bendor-Samuel, 2017). This makes it hard to keep track of the MT research, models and dataset that have been developed for some of them. As the internet and social media make up the daily lives of more than half of the world(Lin, 2020), as well as over 40% of Africans…
Jacobs University Bremen（不来梅雅各布大学）；University of Khartoum（喀土穆大学）
From Word2Vec to GloVe, word embedding models have played key roles in the current state-of-the-art results achieved in Natural Language Processing. Designed to give significant and unique vectorized representations of words and entities, those models have proven to efficiently extract similarities and establish relationships reflecting semantic and contextual meaning among words and entities. African Languages, representing more than 31% of the worldwide spoken languages, have recently been subject to lots of research. However, to the best of our knowledge, there are currently very few to non…
Natural Language Interfaces and tools such as spellcheckers and Web search in one's own language are known to be useful in ICT-mediated communication. Most languages in Southern Africa are under-resourced, however. Therefore, it would be very useful if both the generic and the few language-specific NLP tools could be reused or easily adapted across languages. This depends on the notion, and extent, of similarity between the languages. We assess this from the angle of orthography and corpora. Twelve versions of the Universal Declaration of Human Rights (UDHR) are examined, showing clusters of l…
Masakhane；Africa（非洲）；The State of African NLP（非洲自然语言处理现状（注：此处“The State of African NLP”更可能是论文章节标题而非机构，根据提取规则应忽略，原HTML中作者机构仅明确出现“Masakhane”“masakhane.io”“Africa”，但“Africa”为地理概念非机构，故仅保留“Masakhane”和“masakhane.io”为机构）}]}（注：经再次核查，原HTML中作者机构明确为“Affiliation: Masakhane, Africa”，“Affiliation: masakhane.io”，其中“Africa”属于地理概念非机构，因此仅保留“Masakhane”和“masakhane.io”为作者所属机构，修正后输出应为{）
Africa has over 2000 languages. Despite this, African languages account for a small portion of available resources and publications in Natural Language Processing (NLP). This is due to multiple factors, including: a lack of focus from government and funding, discoverability, a lack of community, sheer language complexity, difficulty in reproducing papers and no benchmarks to compare techniques. To begin to address the identified problems, MASAKHANE, an open-source, continent-wide, distributed, online research effort for machine translation for African languages, was founded. In this paper, we …
Over 800 languages are spoken across West Africa. Despite the obvious diversity among people who speak these languages, one language significantly unifies them all - West African Pidgin English. There are at least 80 million speakers of West African Pidgin English. However, there is no known natural language processing (NLP) work on this language. In this work, we perform the first NLP work on the most popular variant of the language, providing three major contributions. First, the provision of a Pidgin corpus of over 56000 sentences, which is the largest we know of. Secondly, the training of …
Modern speech synthesis techniques can produce natural-sounding speech given sufficient high-quality data and compute resources. However, such data is not readily available for many languages. This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the Text-to-Speech (TTS) systems. We first create a set of general-purpose instructions on building speech synthesis systems with minimum technological resources and subject-matter expertise. Next, we create new datasets and curate datasets from "found" data (existing recordings) thro…
Lelapa AI（莱拉帕人工智能公司）；Luleå University of Technology（吕勒奥理工大学）；Lanfrica（兰弗里卡）；Instituto Politécnico Nacional（墨西哥国立理工学院）；USC Information Sciences Institute（南加州大学信息科学研究所）；University of California, Davis（加州大学戴维斯分校）；New Mexico State University（新墨西哥州立大学）；Technical University of Munich（慕尼黑工业大学）；University of Porto（波尔图大学）；ML Collective（机器学习集体）；University of Toronto（多伦多大学）；University of Dayton（代顿大学）；McGill University（麦吉尔大学）；Mila Quebec AI Institute（米拉魁北克人工智能研究所）
Many natural language processing (NLP) tasks make use of massively pre-trained language models, which are computationally expensive. However, access to high computational resources added to the issue of data scarcity of African languages constitutes a real barrier to research experiments on these languages. In this work, we explore the applicability of low-compute approaches such as language adapters in the context of this low-resource double-bind. We intend to answer the following question: do language adapters allow those who are doubly bound by data and compute to practically build useful m…
Building natural language processing systems for non standardized and low resource languages is a difficult challenge. The recent success of large-scale multilingual pretrained language models provides new modeling tools to tackle this. In this work, we study the ability of multilingual language models to process an unseen dialect. We take user generated North-African Arabic as our case study, a resource-poor dialectal variety of Arabic with frequent code-mixing with French and written in Arabizi, a non-standardized transliteration of Arabic to Latin script. Focusing on two tasks, part-of-spee…
AfroBench: How Good are Large Language Models on African Languages?
Masakhane NLP（Masakhane自然语言处理）；Mila - Quebec AI Institute & McGill University（Mila-魁北克人工智能研究所及麦吉尔大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）；Lelapa AI（Lelapa人工智能）；The African Research Collective（非洲研究集体）；University of Waterloo（滑铁卢大学）；University College London（伦敦大学学院）
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis
Bosch Center for Artificial Intelligence（博世人工智能中心）；Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学语言信息处理中心）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）
Nnamdi Azikiwe University（纳姆迪·阿齐基韦大学）；Federal College of Education (Technical), Umunze（乌蒙泽联邦教育学院（技术类））；University of Sheffield（谢菲尔德大学）
Most languages, especially in Africa, have fewer or no established part-of-speech (POS) tagged corpus. However, POS tagged corpus is essential for natural language processing (NLP) to support advanced researches such as machine translation, speech recognition, etc. Even in cases where there is no POS tagged corpus, there are some languages for which parallel texts are available online. The task of POS tagging a new language corpus with a new tagset usually face a bootstrapping problem at the initial stages of the annotation process. The unavailability of automatic taggers to help the human ann…
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models
Mila, McGill University（米拉实验室、麦吉尔大学）；Lelapa AI（拉拉帕人工智能公司）；Saarland University（萨尔大学）；University of Toronto（多伦多大学）；University College London（伦敦大学学院）；Microsoft Research Africa（微软研究院非洲分部）；Cohere For AI（科here人工智能）；SADiLaR（南非数字语言资源中心）；Ontario Tech University（安大略理工大学）；Lancaster University（兰卡斯特大学）；Princeton University（普林斯顿大学）；Makerere University（马凯雷雷大学）；Leibniz Universität Hannover（莱布尼茨汉诺威大学）；Le CNAM（法国国立工艺学院）；Imperial College London（伦敦帝国学院）；Universidad de Deusto（德乌斯托大学）；DAUST（达累斯萨拉姆科技大学）；Haramaya University（哈拉马亚大学）
HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language
Federal University Dutsin-Ma（杜钦马联邦大学）；Aliko Dangote University of Science and Technology（阿利科·丹格特科技大学）；Northwest University（西北大学）；Bayero University Kano（卡诺巴耶罗大学）
Cross-lingual transfer of multilingual models on low resource African Languages
School of Computer Science and Engineering, Vellore Institute of Technology（韦洛尔理工学院计算机科学与工程学院）；Department of Computer Science, University of Pretoria（比勒陀利亚大学计算机科学系）
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
Mila Quebec AI Institute（米拉魁北克人工智能研究所）；McGill University（麦吉尔大学）；Instituto Politécnico Nacional（国家理工学院）；Uppsala University（乌普萨拉大学）；Universidad de Deusto（德乌斯托大学）；Ashesi University（阿希西大学）；Montclair State University（蒙特克莱尔州立大学）；The College of Saint Rose（圣罗斯学院）；Technical University of Munich（慕尼黑工业大学）
Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
DePaul University（德保罗大学）；University of Washington（华盛顿大学）；Carnegie Mellon University（卡内基梅隆大学）；Cornell Tech University（康奈尔科技大学）；University of Maryland（马里兰大学）
Hausa language belongs to the Afroasiatic phylum, and with more first-language speakers than any other sub-Saharan African language. With a majority of its speakers residing in the Northern and Southern areas of Nigeria and the Republic of Niger, respectively, it is estimated that over 100 million people speak the language. Hence, making it one of the most spoken Chadic language. While Hausa is considered well-studied and documented language among the sub-Saharan African languages, it is viewed as a low resource language from the perspective of natural language processing (NLP) due to limited …
Training data for machine learning models can come from many different sources, which can be of dubious quality. For resource-rich languages like English, there is a lot of data available, so we can afford to throw out the dubious data. For low-resource languages where there is much less data available, we can't necessarily afford to throw out the dubious data, in case we end up with a training set which is too small to train a model. In this study, we examine the effects of text normalization and data set quality for a set of low-resource languages of Africa -- Afrikaans, Amharic, Hausa, Igbo…
The contrast between the need for large amounts of data for current Natural Language Processing (NLP) techniques, and the lack thereof, is accentuated in the case of African languages, most of which are considered low-resource. To help circumvent this issue, we explore techniques exploiting the qualities of morphologically rich languages (MRLs), while leveraging pretrained word vectors in well-resourced languages. In our exploration, we show that a meta-embedding approach combining both pretrained and morphologically-informed word embeddings performs best in the downstream task of Xhosa-Englis…
