David Ifeoluwa Adelani

Hady Elsahar

Constantine Lignos

Atnafu Lambebo Tonja

Salomey Osei

Aremu Anuoluwapo

Clemencia Siro

Shamsuddeen Hassan Muhammad

Tajuddeen Gwadabe

Perez Ogayo

Israel Abebe Azime

Kayode Olaleye

Over 1 billion people live in Africa, and its residents speak more than 2,000 languages. But those languages are among the least represented… (voir plus) in NLP research, and work on African languages is often sidelined at major venues. Over the past few years, a vibrant, collaborative community of researchers has formed around a sustained focus on NLP for the benefit of the African continent: national, regional, continental and even global collaborative efforts focused on African languages, African corpora, and tasks with importance in the African context. The AfricaNLP workshops have been a central venue in organizing, sustaining, and growing this focus, and we propose to continue this tradition with an AfricaNLP 2024 workshop in Vienna. Starting in 2020, the AfricaNLP workshop has become a core event for the African NLP community and has drawn global attendance and interest. Many of the participants are active in the Masakhane grassroots NLP community, allowing the community to convene, showcase and share experiences with each other. Large scale collaborative works have been enabled by participants who joined from the AfricaNLP workshop such as MasakhaNER (61 authors), Quality assessment of Multilingual Datasets (51 authors), Corpora Building for Twi (25 authors), NLP for Ghanaian Languages (25 Authors). Many first-time authors, through the mentorship program, found collaborators and published their first paper. Those mentorship relationships built trust and coherence within the community that continues to this day. We aim to continue this. In the contemporary AI landscape, generative AI has rapidly expanded with significant input and innovation from the global research community. This technology enables machines to generate novel content, showcases potential across a multitude of sectors. However, underrepresentation of African languages persists within this growth. Recognizing the urgency to address this gap has inspired the theme for the 2024 workshop: Adaptation of Generative AI for African languages which aspires to congregate experts, linguists, and AI enthusiasts to delve into solutions, collaborations, and strategies to amplify the presence of African languages in generative AI models.

2024-03-08

ICLR.cc/2024/Workshop_Proposals (publié)

AfriHG: News Headline Generation for African Languages

Toyib Ogunremi

Serah sessi Akojenu

Anthony Soronnadi

Olubayo Adekanmbi

2024-03-03

ICLR.cc/2024/Workshop/AfricaNLP (publié)

ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language Model

Osvaldo Luamba Quinjica

In recent years, the development of pre-trained language models (PLMs) has gained momentum, showcasing their capacity to transcend linguisti… (voir plus)c barriers and facilitate knowledge transfer across diverse languages. However, this progress has predominantly bypassed the inclusion of very-low resource languages, creating a notable void in the multilingual landscape. This paper addresses this gap by introducing four tailored PLMs specifically finetuned for Angolan languages, employing a Multilingual Adaptive Fine-tuning (MAFT) approach. In this paper, we survey the role of informed embedding initialization and synthetic data in enhancing the performance of MAFT models in downstream tasks. We improve baseline over SOTA AfroXLMR-base (developed through MAFT) and OFA (an effective embedding initialization) by 12.3 and 3.8 points respectively.

2024-03-03

ICLR.cc/2024/Workshop/AfricaNLP (publié)

EkoHate: Offensive and Hate Speech Detection for Code-switched Political discussions on Nigerian Twitter

Comfort Eseohen Ilevbare

Jesujoba Oluwadara Alabi

Bakare Firdous Damilola

Abiola Oluwatoyin Bunmi

ADEYEMO Oluwaseyi Adesina

Nigerians have a notable online presence and actively discuss political and topical matters. This was particularly evident throughout the 20… (voir plus)23 general election, where Twitter was utilized for campaigning, fact-checking and verification, and even positive and negative discourse. However, little or none has been done in the detection of abusive language and hate speech in Nigeria. In this paper, we curate code-switched Twitter data directed at three musketeers of the governorship election on the most populous and economically vibrant state in Nigeria; Lagos state, with the view to detect offensive and hate speech on political discussion. We develop EkoHate---an abusive language and hate speech dataset for political discussions between the three candidates and their followers using a binary (normal vs offensive) and fine-grained four-label annotation scheme. We analysed our dataset and provide an empirical evaluation of state-of-the-art methods across both supervised and cross-lingual transfer learning settings. In the supervised setting, our evaluation results in both binary and four-label annotation schemes show that we can achieve 95.1 and 70.3 F1 points respectively. Furthermore, we show that our dataset adequately transfers very well to two publicly available offensive datasets (OLID and HateUS2020) with at least 62.7 F1 points.

2024-03-03

ICLR.cc/2024/Workshop/AfricaNLP (publié)

Enhancing Transformer Models for Igbo Language Processing: A Critical Comparative Study

Anthony Soronnadi

Olubayo Adekanmbi

Chinazo Anebelundu

2024-03-03

ICLR.cc/2024/Workshop/AfricaNLP (publié)

NaijaRC: A Multi-choice Reading Comprehension Dataset for Nigerian Languages

Aremu Anuoluwapo

Jesujoba Oluwadara Alabi

Daud Abolade

Nkechinyere Faith Aguobi

Shamsuddeen Hassan Muhammad

In this paper, we create NaijaRC— a new multi-choice Nigerian Reading Comprehension dataset that is based on high-school RC examination fo… (voir plus)r three Nigerian national languages: Hausa (hau), Igbo (ibo), and \yoruba (yor). We provide baseline results by performing cross-lingual transfer using the Belebele training data which is majorly from RACE {RACE is based on English exams for middle and high school Chinese students, very similar to our dataset.} dataset based on several pre-trained encoder-only models. Additionally, we provide results by prompting large language models (LLMs) like GPT-4.

2024-03-03

ICLR.cc/2024/Workshop/AfricaNLP (publié)

YAD: Leveraging T5 for improved automatic diacritization of Yorùbá text

Akindele Michael Olawole

Jesujoba Oluwadara Alabi

Aderonke Busayo Sakpere

In this work we present Yorùbá automatic diacritization (YAD) benchmark dataset for evaluating Yorùbá diacritization systems. In additio… (voir plus)n, we pre-train text-to-text transformer, T5 model for Yorùbá and showed that this model outperform several multilingually trained T5 models. Lastly, we showed that more data and bigger models are better at diacritization for Yorùbá

2024-03-03

ICLR.cc/2024/Workshop/AfricaNLP (publié)

SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects

Hannah Liu

Xiaoyu Shen

Nikita Vassilyev

Jesujoba Oluwadara Alabi

Yanke Mao

Haonan Gao

Annie En-Shiun Lee

2024-01-01

EACL (1) (publié)

arxiv.org

Cross-lingual Open-Retrieval Question Answering for African Languages

Odunayo Ogundepo

Tajuddeen Gwadabe

Clara E. Rivera

Jonathan H. Clark

Sebastian Ruder

Bonaventure F. P. Dossou

Abdou Aziz DIOP

Claytone Sikasote

Gilles Q. Hacheme

Happy Buzaaba

Ignatius Majesty Ezeani

Rooweither Mabuya

Salomey Osei

Chris Emezue

Albert Njoroge Kahira

Shamsuddeen Hassan Muhammad

Akintunde Oladipo

Abraham Toluwase Owodunni

Atnafu Lambebo Tonja … (voir 24 de plus)

Iyanuoluwa Shode

Akari Asai

Aremu Anuoluwapo

Ayodele Awokoya

Bernard Opoku

Chiamaka Ijeoma Chukwuneke

Christine Mwase

Clemencia Siro

Stephen Arthur

Tunde Oluwaseyi Ajayi

V. Otiende

Andre Niyongabo Rubungo

B. Sinkala

Daniel A. Ajisafe

Emeka Onwuegbuzia

Falalu Lawan

Ibrahim Ahmad

Jesujoba Alabi

CHINEDU EMMANUEL MBONU

Mofetoluwa Adeyemi

Mofya Phiri

Orevaoghene Ahia

Ruqayya Nasir Iro

Sonia Adhiambo

2023-12-01

Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)

Cross-lingual Open-Retrieval Question Answering for African Languages

Odunayo Ogundepo

Tajuddeen Gwadabe

Clara E. Rivera

Jonathan H. Clark

Sebastian Ruder

Bonaventure F. P. Dossou

Abdou Aziz DIOP

Claytone Sikasote

Gilles HACHEME

Happy Buzaaba

Ignatius Ezeani

Rooweither Mabuya

Salomey Osei

Chris Emezue

Albert Kahira

Shamsuddeen Hassan Muhammad

Akintunde Oladipo

Abraham Toluwase Owodunni

Atnafu Lambebo Tonja … (voir 24 de plus)

Iyanuoluwa Shode

Akari Asai

Aremu Anuoluwapo

Ayodele Awokoya

Bernard Opoku

Chiamaka Ijeoma Chukwuneke

Christine Mwase

Clemencia Siro

Stephen Arthur

Tunde Oluwaseyi Ajayi

Verrah Akinyi Otiende

Andre Niyongabo Rubungo

Boyd Sinkala

Daniel Ajisafe

Emeka Felix Onwuegbuzia

Falalu Lawan

Ibrahim Ahmad

Jesujoba Oluwadara Alabi

CHINEDU EMMANUEL MBONU

Mofetoluwa Adeyemi

Mofya Phiri

Orevaoghene Ahia

Ruqayya Nasir Iro

Sonia Adhiambo

2023-12-01

Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)

How good are Large Language Models on African Languages?

Jessica Ojo

Kelechi Ogueji

Pontus Stenetorp

2023-11-14

ArXiv (prépublication)

arxiv.org

Improving Language Plasticity via Pretraining with Active Forgetting

Yihong Chen

Kelly Marchisio

Roberta Raileanu

Pontus Stenetorp

Sebastian Riedel

Mikel Artetxe

Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performan… (voir plus)ce, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue by learning a new embedding layer for the new language, doing so is both data and compute inefficient. We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Concretely, by resetting the embedding layer every K updates during pretraining, we encourage the PLM to improve its ability of learning new embeddings within limited number of updates, similar to a meta-learning effect. Experiments with RoBERTa show that models pretrained with our forgetting mechanism not only demonstrate faster convergence during language adaptation, but also outperform standard ones in a low-data regime, particularly for languages that are distant from English. Code will be available at https://github.com/facebookresearch/language-model-plasticity.

2023-09-21

NeurIPS.cc/2023/Conference (poster)