5 research outputs found
Atar: Attention-based LSTM for Arabizi transliteration
A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49)
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System
Arabic is a complex language with many varieties and dialects spoken by over
450 millions all around the world. Due to the linguistic diversity and
variations, it is challenging to build a robust and generalized ASR system for
Arabic. In this work, we address this gap by developing and demoing a system,
dubbed VoxArabica, for dialect identification (DID) as well as automatic speech
recognition (ASR) of Arabic. We train a wide range of models such as HuBERT
(DID), Whisper, and XLS-R (ASR) in a supervised setting for Arabic DID and ASR
tasks. Our DID models are trained to identify 17 different dialects in addition
to MSA. We finetune our ASR models on MSA, Egyptian, Moroccan, and mixed data.
Additionally, for the remaining dialects in ASR, we provide the option to
choose various models such as Whisper and MMS in a zero-shot setting. We
integrate these models into a single web interface with diverse features such
as audio recording, file upload, model selection, and the option to raise flags
for incorrect outputs. Overall, we believe VoxArabica will be useful for a wide
range of audiences concerned with Arabic research. Our system is currently
running at https://cdce-206-12-100-168.ngrok.io/.Comment: Accepted at ArabicNLP conference co-located with EMNLP'23. First
three authors contributed equall
WojoodNER 2023: The First Arabic Named Entity Recognition Shared Task
We present WojoodNER-2023, the first Arabic Named Entity Recognition (NER)
Shared Task. The primary focus of WojoodNER-2023 is on Arabic NER, offering
novel NER datasets (i.e., Wojood) and the definition of subtasks designed to
facilitate meaningful comparisons between different NER approaches.
WojoodNER-2023 encompassed two Subtasks: FlatNER and NestedNER. A total of 45
unique teams registered for this shared task, with 11 of them actively
participating in the test phase. Specifically, 11 teams participated in
FlatNER, while teams tackled NestedNER. The winning teams achieved F1
scores of 91.96 and 93.73 in FlatNER and NestedNER, respectively