AraSpot: Arabic Spoken Command Spotting

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1. Apparenté Pas pertinent

   AraGPT2: Pre-Trained Transformer for Arabic Language Generation

   Wissam Antoun, Fady Baly, Hazem Hajj · 2020 · Calcul et langage · Pas encore résumé

   Résumé scientifique

   Recently, pre-trained transformer-based architectures have proven to be very efficient at language modeling and understanding, given that they are trained on a large enough corpus. Applications in language generation for Arabic are still lagging in comparison to other NLP advances primarily due to the lack of advanced Arabic language generation models. In this paper, we develop the first advanced Arabic language generation model, AraGPT2, trained from scratch on a large Arabic corpus of internet text and news articles. Our largest model, AraGPT2-mega, has 1.46 billion parameters, which makes it the largest Arabic language model available. The Mega model was evaluated and showed success on di…

2. Apparenté Pas pertinent

   Automatic Arabic Dialect Identification Systems for Written Texts: A Survey

   Maha J. Althobaiti · 2020 · Calcul et langage · Résumé IA disponible

   Résumé scientifique

   Arabic dialect identification is a specific task of natural language processing, aiming to automatically predict the Arabic dialect of a given text. Arabic dialect identification is the first step in various natural language processing applications such as machine translation, multilingual text-to-speech synthesis, and cross-language text generation. Therefore, in the last decade, interest has increased in addressing the problem of Arabic dialect identification. In this paper, we present a comprehensive survey of Arabic dialect identification research in written texts. We first define the problem and its challenges. Then, the survey extensively discusses in a critical manner many aspects rel…

3. Apparenté Pas pertinent

   ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic

   Fajri Koto, Haonan Li, Sara Shatnawi et al. · 2024 · Calcul et langage · Pas encore résumé

   Résumé scientifique

   The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models. While state-of-the-art models are partially trained on large Arabic texts, evaluating their performance in Arabic remains challenging due to the limited availability of relevant datasets. To bridge this gap, we present ArabicMMLU, the first multi-task language understanding benchmark for Arabic language, sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA),…

4. Apparenté Pas pertinent

   Proficiency assessment of L2 spoken English using wav2vec 2.0

   Stefano Bannò, Marco Matassoni · 2022 · Calcul et langage · Résumé IA disponible

   Résumé scientifique

   The increasing demand for learning English as a second language has led to a growing interest in methods for automatically assessing spoken language proficiency. Most approaches use hand-crafted features, but their efficacy relies on their particular underlying assumptions and they risk discarding potentially salient information about proficiency. Other approaches rely on transcriptions produced by ASR systems which may not provide a faithful rendition of a learner's utterance in specific scenarios (e.g., non-native children's spontaneous speech). Furthermore, transcriptions do not yield any information about relevant aspects such as intonation, rhythm or prosody. In this paper, we investiga…

5. Apparenté Pas pertinent

   GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP

   Md Tawkat Islam Khondaker, Abdul Waheed, El Moatez Billah Nagoudi et al. · 2023 · Calcul et langage · Résumé IA disponible

   Résumé scientifique

   ChatGPT's emergence heralds a transformative phase in NLP, particularly demonstrated through its excellent performance on many English benchmarks. However, the model's efficacy across diverse linguistic contexts remains largely uncharted territory. This work aims to bridge this knowledge gap, with a primary focus on assessing ChatGPT's capabilities on Arabic languages and dialectal varieties. Our comprehensive study conducts a large-scale automated and human evaluation of ChatGPT, encompassing 44 distinct language understanding and generation tasks on over 60 different datasets. To our knowledge, this marks the first extensive performance analysis of ChatGPT's deployment in Arabic NLP. Our f…

6. Apparenté Pas pertinent

   Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization

   Bashar Al-Rfooh, Gheith Abandah, Rami Al-Rfou · 2023 · Calcul et langage · Résumé IA disponible

   Résumé scientifique

   Most of previous work on learning diacritization of the Arabic language relied on training models from scratch. In this paper, we investigate how to leverage pre-trained language models to learn diacritization. We finetune token-free pre-trained multilingual models (ByT5) to learn to predict and insert missing diacritics in Arabic text, a complex task that requires understanding the sentence semantics and the morphological structure of the tokens. We show that we can achieve state-of-the-art on the diacritization task with minimal amount of training and no feature engineering, reducing WER by 40%. We release our finetuned models for the greater benefit of the researchers in the community.

7. Apparenté Pas pertinent

   Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages

   Yu Zhang, Wei Han, James Qin et al. · 2023 · Calcul et langage · Résumé IA disponible

   Résumé scientifique

   We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages. This is achieved by pre-training the encoder of the model on a large unlabeled multilingual dataset of 12 million (M) hours spanning over 300 languages, and fine-tuning on a smaller labeled dataset. We use multilingual pre-training with random-projection quantization and speech-text modality matching to achieve state-of-the-art performance on downstream multilingual ASR and speech-to-text translation tasks. We also demonstrate that despite using a labeled training set 1/7-th the size of that used for the Whisper model, our model exhibits comparable or be…

8. Apparenté Pas pertinent

   Filler Word Detection and Classification: A Dataset and Benchmark

   Ge Zhu, Juan-Pablo Caceres, Justin Salamon · 2022 · Calcul et langage · Résumé IA disponible

   Résumé scientifique

   Filler words such as `uh' or `um' are sounds or words people use to signal they are pausing to think. Finding and removing filler words from recordings is a common and tedious task in media editing. Automatically detecting and classifying filler words could greatly aid in this task, but few studies have been published on this problem. A key reason is the absence of a dataset with annotated filler words for training and evaluation. In this work, we present a novel speech dataset, PodcastFillers, with 35K annotated filler words and 50K annotations of other sounds that commonly occur in podcasts such as breaths, laughter, and word repetitions. We propose a pipeline that leverages VAD and ASR to…