Problem
Research questions and friction points this paper is trying to address.
Arabic speech
Named Entity Recognition
end-to-end NER
low-resource
morphological complexity
CV-18 NER: Augmented Common Voice for Named Entity Recognition from Arabic Speech
🤖 AI Summary
This study addresses the challenge of end-to-end named entity recognition (NER) from Arabic speech, which is hindered by morphological complexity, vowel omission, and scarce annotated resources. The authors introduce CV-18 NER, the first publicly available Arabic speech NER dataset, annotated with the fine-grained Wojood schema comprising 21 entity types. They systematically evaluate both pipeline and end-to-end approaches, demonstrating that end-to-end models significantly outperform traditional cascaded systems. Specifically, AraBEST-RQ (300M), leveraging Arabic self-supervised pretraining, achieves a CoER of 37.0%, while Whisper-medium attains 38.0% CVER. The work further highlights the efficacy of multilingual weak supervision for cross-lingual transfer in low-resource settings. The dataset and code are publicly released to support future research.
📝 Abstract
End-to-end speech Named Entity Recognition (NER) aims to directly extract entities from speech. Prior work has shown that end-to-end (E2E) approaches can outperform cascaded pipelines for English, French, and Chinese, but Arabic remains under-explored due to its morphological complexity, the absence of short vowels, and limited annotated resources. We introduce CV-18 NER, the first publicly available dataset for NER from Arabic speech, created by augmenting the Arabic Common Voice 18 corpus with manual NER annotations following the fine-grained Wojood schema (21 entity types). We benchmark both pipeline systems (ASR + text NER) and E2E models based on Whisper and AraBEST-RQ. E2E systems substantially outperform the best pipeline configuration on the test set, reaching 37.0% CoER (AraBEST-RQ 300M) and 38.0% CVER (Whisper-medium). Further analysis shows that Arabic-specific self-supervised pretraining yields strong ASR performance, while multilingual weak supervision transfers more effectively to joint speech-to-entity learning, and that larger models may be harder to adapt in this low-resource setting. Our dataset and models are publicly released, providing the first open benchmark for end-to-end named entity recognition from Arabic speech https://huggingface.co/datasets/Elyadata/CV18-NER.
Innovation
Methods, ideas, or system contributions that make the work stand out.
end-to-end speech NER
Arabic speech processing
CV-18 NER dataset
self-supervised pretraining
multilingual weak supervision
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