11 research outputs found
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Africa is home to over 2000 languages from over six language families and has
the highest linguistic diversity among all continents. This includes 75
languages with at least one million speakers each. Yet, there is little NLP
research conducted on African languages. Crucial in enabling such research is
the availability of high-quality annotated datasets. In this paper, we
introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets
in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda,
Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili,
Tigrinya, Twi, Xitsonga, and Yor\`ub\'a) from four language families annotated
by native speakers. The data is used in SemEval 2023 Task 12, the first
Afro-centric SemEval shared task. We describe the data collection methodology,
annotation process, and related challenges when curating each of the datasets.
We conduct experiments with different sentiment classification baselines and
discuss their usefulness. We hope AfriSenti enables new work on
under-represented languages. The dataset is available at
https://github.com/afrisenti-semeval/afrisent-semeval-2023 and can also be
loaded as a huggingface datasets
(https://huggingface.co/datasets/shmuhammad/AfriSenti).Comment: 15 pages, 6 Figures, 9 Table
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The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who
Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation. Nevertheless, few papers thoroughly discuss how. We document this by analysing 100 highly-cited papers, and annotating epistemic elements related to intended use, i.e., means, ends, and stakeholders. We find that narratives leaving out some of these aspects are common, that many papers propose inconsistent means and ends, and that the feasibility of suggested strategies rarely has empirical backing. We argue that this vagueness actively hinders the technology from reaching its goals, as it encourages overclaiming, limits criticism, and prevents stakeholder feedback. Accordingly, we provide several recommendations for thinking and writing about the use of fact-checking artefacts
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Varifocal Question Generation for Fact-checking
Fact-checking requires retrieving evidence related to a claim under investigation. The task can be formulated as question generation based on a claim, followed by question answering. However, recent question generation approaches assume that the answer is known and typically contained in a passage given as input, whereas such passages are what is being sought when verifying a claim. In this paper, we present Varifocal, a method that generates questions based on different focal points within a given claim, i.e. different spans of the claim and its metadata, such as its source and date. Our method outperforms previous work on a fact-checking question generation dataset on a wide range of automatic evaluation metrics. These results are corroborated by our manual evaluation, which indicates that our method generates more relevant and informative questions. We further demonstrate the potential of focal points in generating sets of clarification questions for product descriptions
Wheeze phenotypes in young children have different courses during the preschool period
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