This paper introduces a large-scale historical corpus of written Arabic spanning 1400 years. The corpus was cleaned and processed using Arabic NLP tools, including identification of reused text. The study uses a novel automatic periodization algorithm to study the history of the Arabic language, confirming the division into Modern Standard and Classical Arabic. Why it matters: This resource enables further computational research into the evolution of Arabic and the development of NLP tools for historical texts.
Researchers created a cross-cultural corpus of annotated verbal and nonverbal behaviors in receptionist interactions. The corpus includes native speakers of American English and Arabic role-playing scenarios at university reception desks in Doha, Qatar, and Pittsburgh, USA. The manually annotated nonverbal behaviors include gaze direction, hand gestures, torso positions, and facial expressions. Why it matters: This resource can be valuable for the human-robot interaction community, especially for building culturally aware AI systems.
This paper introduces a new task: detecting propaganda techniques in code-switched text. The authors created and released a corpus of 1,030 English-Roman Urdu code-switched texts annotated with 20 propaganda techniques. Experiments show the importance of directly modeling multilinguality and using the right fine-tuning strategy for this task.
The ArabJobs dataset is a new corpus of over 8,500 Arabic job advertisements collected from Egypt, Jordan, Saudi Arabia, and the UAE. The dataset contains over 550,000 words and captures linguistic, regional, and socio-economic variation in the Arab labor market. It is available on GitHub and can be used for fairness-aware Arabic NLP and labor market research.
This study investigates the correlation between Google Trends data for COVID-19 symptoms and the actual number of COVID-19 cases in Saudi Arabia between March and October 2020. The researchers found that searches for "cough" and "sore throat" were most frequent, while "loss of smell", "loss of taste", and "diarrhea" showed the highest correlation with confirmed cases. The study concludes that Google searches can serve as a supplementary surveillance tool for monitoring the spread of COVID-19 in Saudi Arabia. Why it matters: The research demonstrates the potential of using readily available digital data to augment traditional surveillance methods for public health monitoring in the region.
NYU and NYU Abu Dhabi researchers are working on user-centric gender rewriting in NLP, especially for Arabic. They are building an Arabic Parallel Gender Corpus and developing models for gender rewriting tasks. The work aims to address representational harms caused by NLP systems that don't account for user preferences regarding grammatical gender. Why it matters: This research promotes fairness and inclusivity in Arabic NLP by enabling systems to generate gender-specific outputs based on user preferences, mitigating biases present in training data.