Austria

The development of Large Language Models (LLMs) for African languages has been hindered by the lack of large-scale textual data. Previous research has shown that relatively small language models, when trained on synthetic data generated by larger models, can produce fluent, short English stories, providing a data-efficient alternative to large-scale pretraining. In this paper, we apply a similar approach to develop and evaluate small language models for generating children’s stories in isiZulu and Yoruba, using synthetic datasets created through translation and multilingual prompting. We train six language-specific models varying in dataset size and source, and based on the GPT-2 architecture. Our results show that models trained on synthetic low-resource data are capable of producing coherent and fluent short stories in isiZulu and Yoruba. Models trained on larger synthetic datasets generally perform better in terms of coherence and grammar, and also tend to generalize better, as seen by their lower evaluation perplexities. Models trained on datasets generated through prompting instead of translation generate similar or more coherent stories and display more creativity, but perform worse in terms of generalization to unseen data. In addition to the potential educational applications of the automated story generation, our approach has the potential to be used as the foundation for more data-efficient low-resource language models.

ACL 2025

Story Generation with Large Language Models for African Languages

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Understanding written content can vary significantly based on the linguistic complexity of the text. In the context of Amharic, a morphologically rich and low-resource language, the use of complex vocabulary and less frequent expressions often hinders understanding, particularly among readers with limited literacy skills. Such complexity poses challenges for both human comprehension and NLP applications. Addressing this complexity in Amharic is therefore important for text readability and accessibility. In this study, we developed a text complexity annotation tool using curated list of 1,113 complex Amharic terms. Utilizing this tool, we collected and annotated a dataset comprising 20,000 sentences. Based on the annotated corpus, we developed a text complexity classification model using both traditional and deep learning approaches. For traditional machine learning models, the dataset was vectorized using the Bag-of-Words representation. For deep learning and pre-trained models, we implemented embedding layers based on Word2Vec and BERT, trained on a vocabulary consisting of 24,148 tokens. The experiment is conducted using Support Vector Machine and Random Forest for classical machine learning, and Long Short-Term Memory, Bidirectional LSTM, and BERT for deep learning and pre-trained models. The classification accuracies achieved were 83.5% for SVM, 80.3% for RF, 84.1% for LSTM, 85.0% for BiLSTM, and 89.4% for the BERT-based model. Among these, the BERT-based approaches shows optimal performance for text complexity classifications which have ability to capture long-range dependencies and contextual relationships within the text.

Supervised Machine Learning based Amharic Text Complexity Classification Using Automatic Annotator Tool

In this paper, we propose a comprehensive framework for the classification of Swahili news articles using a combination of classical machine learning techniques, deep neural networks, and transformer-based models. By balancing two diverse datasets sourced from Harvard Dataverse and Kaggle, our approach addresses the inherent challenges of imbalanced data in low-resource languages. Our experiments demonstrate the effectiveness of the proposed methodology and set the stage for further advances in Swahili natural language processing.

Swahili News Classification: Performance, Challenges, and Explainability Across ML, DL, and Transformers

The purpose of this work is to share an English-Yorùbá evaluation dataset for openbook reading comprehension with open-ended questions to assess the performance of models both in a high- and a low-resource language. The dataset contains 358 questions and answers on 338 English documents and 208 Yorùbá documents. Experiments show a consistent disparity in performance between the two languages, with Yorùbá falling behind English for automatic metrics even if documents are much shorter for this language. For a small set of documents with comparable length, performance of Yorùbá drops by 2.5 times and this comparison is validated with human evaluation. When analyzing performance by length, we observe that Yorùbá decreases performance dramatically for documents that reach 1500 words while English performance is barely affected at that length. Our dataset opens the door to showcasing if English LLM reading comprehension capabilities extend to Yorùbá, which for the evaluated LLMs is not the case.

Y-NQ: English-Yorùbá Evaluation dataset for Open-Book Reading Comprehension with Open-Ended Questions

This paper presents Yankari, a large-scale monolingual dataset for the Yoruba language, aimed at addressing the critical gap in Natural Language Processing (NLP) resources for this important West African language. Despite being spoken by over 30 million people, Yoruba has been severely underrepresented in NLP research and applications. We detail our methodology for creating this dataset, which includes careful source selection, automated quality control, and rigorous data cleaning processes. The Yankari dataset comprises 51,407 documents from 13 diverse sources, totaling over 30 million tokens. Our approach focuses on ethical data collection practices, avoiding problematic sources and addressing issues prevalent in existing datasets. We provide thorough automated evaluations of the dataset, demonstrating its quality compared to existing resources. The Yankari dataset represents a significant advancement in Yoruba language resources, providing a foundation for developing more accurate NLP models, supporting comparative linguistic studies, and contributing to the digital accessibility of the Yoruba language.

Yankari: Monolingual Yoruba Dataset

Most of the 40+ languages spoken in Togo are severely under-represented in Natural Language Processing (NLP) resources. We present YodiV3, a comprehensive approach to developing NLP for ten Togolese languages (plus two major lingua francas) covering machine translation, speech recognition, text-to-speech, and language identification. We introduce Eyaa-Tom, a new multi-domain parallel corpus (religious, healthcare, financial, etc.) for these languages. We also propose the Lom metric, a scoring framework to quantify the AI-readiness of each language in terms of available resources. Our experiments demonstrate that leveraging large pretrained models (e.g.NLLB for translation, MMS for speech) with YodiV3 leads to significant improvements in low-resource translation and speech tasks. This work highlights the impact of integrating diverse data sources and pretrained models to bootstrap NLP for under-served languages, and outlines future steps for expanding coverage and capability.

YodiV3: NLP for Togolese Languages with Eyaa-Tom Dataset and the Lom Metric

Large-scale multilingual evaluations, such as MEGA, often include only a handful of African languages due to the scarcity of high-quality evaluation data and the limited discoverability of existing African datasets. This lack of representation hinders comprehensive LLM evaluation across a diverse range of languages and tasks. To address these challenges, we introduce AFROBENCH—a multi-task benchmark for evaluating the performance of LLMs across 64 African languages, 15 tasks and 22 datasets. AFROBENCH consists of nine natural language understanding datasets, six text generation datasets, six knowledge and question answering tasks, and one mathematical reasoning task. We present results comparing the performance of prompting LLMs to fine-tuned baselines based on BERT and T5-style models. Our results suggest large gaps in performance between high-resource languages, such as English, and African languages across most tasks; but performance also varies based on the availability of monolingual data resources. Our findings confirm that performance on African languages continues to remain a hurdle for current LLMs, underscoring the need for additional efforts to close this gap.

AfroBench: How Good are Large Language Models on African Languages?

The fact that everyone with a social media account can create and share content, and the increasing public reliance on social media platforms as a news and information source bring about significant challenges such as misinformation, fake news, harmful content, etc. Although human content moderation may be useful to an extent and used by these platforms to flag posted materials, the use of AI models provides a more sustainable, scalable, and effective way to mitigate these harmful contents. However, low-resourced languages such as the Somali language face limitations in AI automation, including scarce annotated training datasets and lack of language models tailored to their unique linguistic characteristics. This paper presents part of our ongoing research work to bridge some of these gaps for the Somali language. In particular, we created two human-annotated social-media-sourced Somali datasets for two downstream applications, fake news & toxicity classification, and developed a transformer-based monolingual Somali language model (named SomBERTa) -- the first of its kind to the best of our knowledge. SomBERTa is then fine-tuned and evaluated on toxic content, fake news and news topic classification datasets. Comparative evaluation analysis of the proposed model against related multilingual models (e.g., AfriBERTa, AfroXLMR, etc) demonstrated that SomBERTa consistently outperformed these comparators in both fake news and toxic content classification tasks while achieving the best average accuracy (87.99%) across all tasks. This research contributes to Somali NLP by offering a foundational language model and a replicable framework for other low-resource languages, promoting digital and AI inclusivity and linguistic diversity.

Detection of Somali-written Fake News and Toxic Messages on the Social Media Using Transformer-based Language Models

Large language models (LLMs) are effective in predicting the labels of unseen target instances if instructed for the task and training instances via the prompt. LLMs generate a text with higher probability if the prompt contains text with similar characteristics, a phenomenon, called priming, that especially affects argumentation. An open question in NLP is how to systematically exploit priming to choose a set of instances suitable for a given task. For stance classification, LLMs may be primed with few-shot instances prior to identifying whether a given argument is pro or con a topic. In this paper, we explore two priming strategies for few-shot stance classification: one takes those instances that are most semantically similar, and the other chooses those that are most stance-similar. Experiments on three common stance datasets suggest that priming an LLM with stance-similar instances is particularly effective in few-shot stance classification compared to baseline strategies, and behaves largely consistently across different LLM variants.

Exploring LLM Priming Strategies for Few-Shot Stance Classification

In this position paper, we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, at present, large language models (LLMs) are inadequate for this purpose, and we propose an ideal technology design aimed at enhancing argumentative skills. This involves re-framing LLMs as tools to exercise our critical thinking skills rather than replacing them. We introduce the concept of reasonable parrots that embody the fundamental principles of relevance, responsibility, and freedom, and that interact through argumentative dialogical moves. These principles and moves arise out of millennia of work in argumentation theory and should serve as the starting point for LLM-based technology that incorporates basic principles of argumentation.

Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design

Through manual annotation or automated argument mining processes, arguments can be represented not only as text, but also in structured formats like graphs. When searching for relevant arguments, this additional information about the relationship between their elementary units allows for the formulation of fine-grained structural constraints by using graphs as queries. Then, a retrieval can be performed by computing the similarity between the query and all available arguments. Previous works employed Graph Edit Distance (GED) algorithms such as A* search to compute mappings between nodes and edges for determining the similarity, which is rather expensive. In this paper, we propose an alternative based on Vision Transformers where arguments are rendered as images to obtain dense embeddings. We propose multiple space-filling visualizations and evaluate the retrieval performance of the vision-based approach against an existing A* search-based method. We find that our technique runs orders of magnitude faster than A* search and scales well on larger argument graphs while achieving competitive results.

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