Morocco

Text-to-Speech (TTS) technology offers potential to improve exam accessibility for visually impaired learners, but existing systems often underperform in underrepresented languages like Yoruba. This study evaluates current Yoruba TTS models in delivering standardized exam content to five visually impaired students through a web-based interface. Before testing, four Yoruba TTS systems were compared; only Facebook’s mms-tts-yor and YarnGPT produced intelligible Yoruba speech. Students experienced exam questions delivered by human voice, Braille, and TTS. All preferred Braille for clarity and independence, some valued human narration, while TTS was least favored due to robotic and unclear output. These results reveal a significant gap between TTS capabilities and the needs of users in low-resource languages. The paper highlights the urgency of developing tone-aware, user-centered TTS solutions to ensure equitable access to digital education for visually impaired speakers of underrepresented languages

EACL 2026

Can Text-to-Speech Systems enable Inclusive Computer-Based Testing? An Evaluation of Yoruba TTS for Visually Impaired Learners

text-to-speech

accessibility

visually impaired

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

workshop paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Climate change refers to variations in temperature and weather conditions due to various climate-related factors on earth.These factors vary across regions, and people's perceptions of climate change. Analyzing public opinion on climate change at a regional level is crucial for developing targeted solutions. However, manually analyzing large volumes of data is challenging for informed dissension. Applying emerging pre-trained Large Language Models offers a promising solution for efficiently analyzing large datasets and understanding public perspectives on climate change. Amharic is one of the widely spoken African languages. Many speakers of the language are actively discussing and expressing their opinions on various topics, including climate change, on social media. Given the increasing discussions about climate change, this study focuses on the sentiment analysis of Amharic climate texts. We collected 6013 sentences from social media and news sources. The data is annotated manually by native speakers to its target polarity. We conducted experiments using the LLM that supports African languages during pre-training. In this study, MultilingualBert and AfriBERTa models were employed with hyperparameter tuning to perform sentiment polarity analysis on Amharic climate text. The experimental results shows that MultilingualBert outperforms AfriBERTa, achieving an accuracy of 69%. This performance is attributed to MultilingualBert’s enhanced capability to capture token-level semantics by giving a variety of attention across tokens, thereby improving its contextual understanding in downstream sentiment classification tasks

Analyzing Sentiment Polarity in Amharic Climate Change Discussions Using Large Language Models

Given the advancement of various Artificial Intelligence (AI) technologies in the 21st century, Automatic Speech Recognition (ASR) plays a vital role in human and machine interaction and serves as an interface for a wide range of applications. The development of these high-performing, robust and useful technologies continue to gain more attention on high-resource languages due to high availability of language data, market profitability dominance and access to funding and research initiatives compared to the marginalised low-resource languages. Despite efforts to develop ASR systems for African languages, there are still numerous challenges due to limited speech datasets, tonal complexity and dialectal variation. In this study, we curated a domain-specific speech dataset for one of the oral Yoruba literatures, proverbs, which are highly culturally inclined. We used the Yoruba recording app that was developed for Iroyin-speech project to record 6 hours of Yoruba proverb sentences. The NCAIR1/Yoruba-ASR model which was finetuned on Open AI Whisper Small and Massively Multilingual Speech, a multilingual speech model featuring low-resource languages including Yoruba language was evaluated with the recorded Yoruba proverbs. Evaluation was conducted based on Word Error Rate (WER) and Tone Error Rate (TER). Our result shows that current ASR systems that support Yoruba does not capture cultural nuances. These findings highlight an urgent need to curate more robust speech datasets that are culturally embedded for low resource languages and in this case particularly, Yoruba language in order to build technological tools that preserve African culture, language and identity.

ÒWE-Voice: An Evaluation of Monolingual and Multilingual ASR Model Using Yoruba Proverb Speech Dataset

Travel agencies in many African countries face increasing pressure to handle large volumes of customer inquiries with limited staff or, either non-existent or outdated rule-based chat-bots. To address this challenge, we develop a conversational virtual assistant powered by a Large Language Model (LLM) and enhanced with a Retrieval-Augmented Generation (RAG) pipeline. The system combines LLM reasoning, company-specific knowledge retrieval, and real-time API (Application Programming Interface) integration to deliver accurate, context-aware responses through WhatsApp, the region’s most widely used communication platform. A dedicated web interface enables staff to upload and update internal documents, ensuring that the assistant remains aligned with changing service information. Demonstrations show that the proposed solution improves response speed, enhances user experience, and reduces operational burden.

Building a Conversational AI Assistant for African Travel Services with LLMs and RAG

In recent times, artificial intelligence (AI) systems have become the primary intermediary to information access, services, and opportunities. Currently, there are growing concerns as to how existing social inequalities are reproduced and amplified through AI. This is significantly evident in language technologies, where a small number of dominant languages or what we'll refer to as big languages and cultural contexts shape the training, design, and evaluation of models. This paper examines the intersections of power asymmetries, linguistic bias, and cultural representation in AI, with a major focus on African languages and communities. We argue that current Natural Language Processing (NLP) systems reflect a high level of global imbalances in the availability of data, infrastructure, and decision making power, often marginalizing low-resourced languages and cultural peculiarities. It is important we know that how these data are structured is a great determinant in what their outcome will be. With reference to examples from speech recognition, machine translation, and large language models, we highlight the social and cultural consequences of linguistic exclusion, including reduced accessibility, misinterpretation, and digital invisibility. Finally, we identify and discuss pathways toward more equitable language technologies, emphasizing community-led data practices, interdisciplinary collaboration, and context-aware evaluation frameworks. By foregrounding language as both a technical and political concern, this work advocates for African-centered approaches to NLP that promote fairness, accountability, and linguistic justice in AI development.

Power Asymmetries, Bias, and AI, a Reflection of Society on Low-Resourced Languages - African Languages as Case Study

In this work, we introduce Sudanese-Flores, an extension of the popular Flores+ machine translation (MT) benchmark to the Sudanese Arabic dialect. We translate both the DEV and DEVTEST splits of the Modern Standard Arabic dataset into the corresponding Sudanese dialect, resulting in a total of 2,009 sentences. While the dialect was recently introduced in Google Translate, there are no available benchmark in this dialect despite spoken by over 40 million people. Our evaluation on two leading LLMs such as GPT-4.1 and Gemini 2.5 Flash showed that while the performance English to Arabic is impressive (more than 23 BLEU), they struggle on Sudanese dialect (less than 11 BLEU) in zero-shot settings. In few-shot scenario, we achieved only a slight boost in performance.

Sudanese-Flores: Extending FLORES+ to Sudanese Arabic Dialect

Automatic Speech Recognition (ASR) systems perform well for high-resource languages, but most African languages, including Wolof, remain underrepresented, particularly in maternal and reproductive healthcare. This work proposes a domain-specific approach to improving Wolof ASR under low-resource conditions, addressing limited annotated data, orthographic variability, and code-switching. We curated a dataset of 750 validated Wolof utterances covering 250 maternal health keywords and applied data augmentation to increase acoustic diversity. Pretrained models, including wav2vec~2.0 and Whisper, were benchmarked to select candidates for fine-tuning. Using parameter-efficient Low-Rank Adaptation (LoRA), a Whisper model was adapted to the maternal health domain. Evaluation using Word Error Rate (WER), Character Error Rate (CER), and Keyword Error Rate (KER), which measures medically critical term transcription accuracy, shows substantial gains, reducing WER from 46.5% to 23.2\% and KER from 17\% to 11%. Community-based evaluation on 1,340 real-world utterances reveals a moderate degradation, with WER increasing by 35%. These results demonstrate that lightweight domain adaptation with small, high-quality data can significantly improve ASR for low-resource healthcare applications.This work introduces one of the first Wolof ASR datasets for healthcare and presents a practical framework for developing reliable speech recognition tools in underrepresented languages, improving access to healthcare information and services.

Enhancing Automatic Speech Recognition Models for Maternal and Reproductive Health: Fine-Tuning and Real-World Evaluation in Wolof

Wikipedia editors undertake the task of editing machine translation (MT) outputs in various languages to disseminate multilingual knowledge from English. But are editors doing more than just translating or fixing MT output? To answer this broad question, we constructed a dataset of 4,335 fine-grained annotated parallel pairs of MT translations and human post-edit (HE) translations for five low-resource African languages: Hausa, Igbo, Swahili, Yoruba, and Zulu. We report on our data selection and annotation methodologies as well as findings from the annotated dataset, the most surprising of which is that annotators mostly preferred the MT translations over their HE counterparts for three out of five languages. We analyze the nature of these "fluency breaking" edits and provide recommendations for the MT post-editing workflows in the Wikipedia domain and beyond.

Evaluating Native-Speaker Preferences on Machine Translation and Post-Edits for Five African Languages

Effective text generation and chat interfaces for low-resource languages (LRLs) remain a challenge for state-of-the-art large language models (LLMs) to support. This is mainly due to the difficulty of curating high-quality instruction datasets for LRLs, a limitation prevalent in the languages spoken across the African continent and other regions. Current approaches, such as automated translation and synthetic data generation, frequently yield outputs that lack fluency or even orthographic consistency. In this paper, we introduce InstructLR, a novel framework designed to generate high-quality instruction datasets for LRLs. Our approach integrates LLM-driven text generation with a dual-layer quality filtering mechanism: an automated filtering layer based on retrieval-augmented-generation (RAG)-based n-shot prompting, and a human-in-the-loop validation layer. Drawing inspiration from benchmarks such as MMLU in task definition, InstructLR has facilitated the creation of three multi-domain instruction benchmarks: **ZarmaInstruct-50k**, **BambaraInstruct-50k**, and **FulfuldeInstruct-50k**.

InstructLR: A Scalable Approach to Create Instruction Dataset for Under-Resourced Languages

African languages have very little labelled data, and it is unclear if augmenting the quantity of annotation data reliably enhances downstream performance. The study is a systematic sample-size scaling study of natural language inference (NLI) on 16 African languages based on the AfriXNLI benchmark. Under controlled conditions, two multilingual transformer models with roughly 0.6B parameters XLM-R Large fine-tuned on XNLI and AfroXLM-R Large are tested on sample sizes of between 50 and 500 labeled examples and average their results across random subsampling runs. As opposed to the usual belief of monotonic increase with increased data, we find a strongly language-sensitive and often non-monotonic scaling behavior. Some languages show early saturation or decrease in performance with sample size as well as high variance in low resource regimes. These results indicate that the volume of data is not enough to guarantee stable profits to African NLI, creating the necessity of language-sensitive datasets creation and stronger multi-lingual modelling strategies.

Sample-Size Scaling of the African Languages NLI Evaluation

Bilingual Lexicon Induction (BLI) is a valuable tool in machine translation and cross-lingual transfer learning, but it remains challenging for agglutinative and low-resource languages. In this work, we investigate the use of weighted sub-word embeddings in BLI for agglutinative languages. We further evaluate a graph-matching and Procrustes-based BLI approach on two Bantu languages, assessing its effectiveness in a previously underexplored language family. Our results for Swahili with an average P@1 score of $51.84$% for a $3000$ word dictionary demonstrate the success of the approach for Bantu languages. Weighted sub-word embeddings perform competitively on Swahili and outperform word embeddings in our experiments with Zulu.

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