Year: 2024
Type: conference-paper
Abstract: We introduce DiaSet, a novel dataset of dialectical Arabic speech, manually transcribed and annotated for two specific downstream tasks: sentiment analysis and named entity recognition.The dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan.Our dataset incorporates authentic conversations between YouTube influencers and their respective guests.Furthermore, we have enriched the dataset with simulated conversations initiated by inviting participants from various locales within the said regions.The participants were encouraged to engage in dialogues with our interviewer.Overall, DiaSet consists of 644.8K tokens and 23.2K annotated instances.Uniform writing standards were upheld during the transcription process.Additionally, we established baseline models by leveraging some of the pre-existing Arabic BERT language models, showcasing the potential applications and efficiencies of our dataset.We make DiaSet publicly available for further research.
Institutions: Ben-Gurion University of the Negev, Reichman University
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7131786764