Search Results for author:
Found 68 papers, 7 papers with code
Overview of OSACT5 Shared Task on Arabic Offensive Language and Hate Speech Detection
This paper provides an overview of the shard task on detecting offensive language, hate speech, and fine-grained hate speech at the fifth workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5).
QADI: Arabic Dialect Identification in the Wild
For extrinsic evaluation, we are able to build effective country level dialect identification on tweets with a macro-averaged F1-score of 60. 6% across 18 classes.
Adult Content Detection on Arabic Twitter: Analysis and Experiments
With Twitter being one of the most popular social media platforms in the Arab region, it is not surprising to find accounts that post adult content in Arabic tweets; despite the fact that these platforms dissuade users from such content.
UL2C: Mapping User Locations to Countries on Arabic Twitter
Mapping user locations to countries can be useful for many applications such as dialect identification, author profiling, recommendation system, etc.
Beyond Orthography: Automatic Recovery of Short Vowels and Dialectal Sounds in Arabic
Phonetically correct transcribed speech resources for dialectal Arabic are scarce.
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text
We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023.
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language.
LAraBench: Benchmarking Arabic AI with Large Language Models
Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.
QVoice: Arabic Speech Pronunciation Learning Application
This paper introduces a novel Arabic pronunciation learning application QVoice, powered with end-to-end mispronunciation detection and feedback generator module.
Detecting and Reasoning of Deleted Tweets before they are Posted
Social media platforms empower us in several ways, from information dissemination to consumption.
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition
Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic
Thus, there has been a lot of recent research on automatic detection of propaganda techniques in text as well as in memes.
SpeechBlender: Speech Augmentation Framework for Mispronunciation Data Generation
Our proposed technique achieves state-of-the-art results, with Speechocean762, on ASR dependent mispronunciation detection models at phoneme level, with a 2. 0% gain in Pearson Correlation Coefficient (PCC) compared to the previous state-of-the-art [1].
Ranked #4 on
Phone-level pronunciation scoring
on speechocean762
NatiQ: An End-to-end Text-to-Speech System for Arabic
We concatenated Tacotron1 with the WaveRNN vocoder, Tacotron2 with the WaveGlow vocoder and ESPnet transformer with the parallel wavegan vocoder to synthesize waveforms from the spectrograms.
ArabGend: Gender Analysis and Inference on Arabic Twitter
Gender analysis of Twitter can reveal important socio-cultural differences between male and female users.
Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech
We evaluate our models on external datasets - a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube and Facebook, for assessing generalization capability.
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination
We studied the data for individual types of tweets and temporal changes in stance towards vaccine.
Automatic Expansion and Retargeting of Arabic Offensive Language Training
Rampant use of offensive language on social media led to recent efforts on automatic identification of such language.
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus
We also report the first baseline for Arabic punctuation restoration.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus
We also report the first baseline for Arabic punctuation restoration.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
ASAD: Arabic Social media Analytics and unDerstanding
This system demonstration paper describes ASAD: Arabic Social media Analysis and unDerstanding, a suite of seven individual modules that allows users to determine dialects, sentiment, news category, offensiveness, hate speech, adult content, and spam in Arabic tweets.
Pre-Training BERT on Arabic Tweets: Practical Considerations
The experiments highlight the centrality of data diversity and the efficacy of linguistically aware segmentation.
ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic
Over the past few months, there were huge numbers of circulating tweets and discussions about Coronavirus (COVID-19) in the Arab region.
Arabic Curriculum Analysis
Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes.
ALT at SemEval-2020 Task 12: Arabic and English Offensive Language Identification in Social Media
This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media.
A Panoramic Survey of Natural Language Processing in the Arab World
The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design.
Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms
With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories.
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
We present the results and main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2020).
Arabic Dialect Identification in the Wild
We present QADI, an automatically collected dataset of tweets belonging to a wide range of country-level Arabic dialects -covering 18 different countries in the Middle East and North Africa region.
A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection
Hence, it is important to detect offensive comments in social media platforms.
ALT Submission for OSACT Shared Task on Offensive Language Detection
In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection.
Constructing a Bilingual Corpus of Parallel Tweets
In this paper, we introduce a generic method for collecting parallel tweets.
Overview of OSACT4 Arabic Offensive Language Detection Shared Task
This paper provides an overview of the offensive language detection shared task at the 4th workshop on Open-Source Arabic Corpora and Processing Tools (OSACT4).
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.
Arabic Offensive Language on Twitter: Analysis and Experiments
Detecting offensive language on Twitter has many applications ranging from detecting/predicting bullying to measuring polarization.
Arabic Diacritic Recovery Using a Feature-Rich biLSTM Model
Our model surpasses all previous state-of-the-art systems with a CW error rate (CWER) of 2. 86\% and a CE error rate (CEER) of 3. 7% for Modern Standard Arabic (MSA) and CWER of 2. 2% and CEER of 2. 5% for Classical Arabic (CA).
SemEval-2016 Task 3: Community Question Answering
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic.
SemEval-2017 Task 3: Community Question Answering
We describe SemEval-2017 Task 3 on Community Question Answering.
A System for Diacritizing Four Varieties of Arabic
Short vowels, aka diacritics, are more often omitted when writing different varieties of Arabic including Modern Standard Arabic (MSA), Classical Arabic (CA), and Dialectal Arabic (DA).
POS Tagging for Improving Code-Switching Identification in Arabic
When speakers code-switch between their native language and a second language or language variant, they follow a syntactic pattern where words and phrases from the embedded language are inserted into the matrix language.
QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification
This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification).
Highly Effective Arabic Diacritization using Sequence to Sequence Modeling
Arabic text is typically written without short vowels (or diacritics).
Diacritization of Maghrebi Arabic Sub-Dialects
Diacritization process attempt to restore the short vowels in Arabic written text; which typically are omitted.
Build Fast and Accurate Lemmatization for Arabic
In this paper we describe the complexity of building a lemmatizer for Arabic which has a rich and complex derivational morphology, and we discuss the need for a fast and accurate lammatization to enhance Arabic Information Retrieval (IR) results.
Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM
Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval.
Ranked #1 on
Sentiment Analysis
on DynaSent
(using extra training data)
Abusive Language Detection on Arabic Social Media
We expand the list of obscene words using this classification, and we report results on a newly created dataset of classified Arabic tweets (obscene, offensive, and clean).
Learning from Relatives: Unified Dialectal Arabic Segmentation
Arabic dialects do not just share a common koin{\'e}, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other.
QCRI Live Speech Translation System
This paper presents QCRI{'}s Arabic-to-English live speech translation system.
Arabic Diacritization: Stats, Rules, and Hacks
In this paper, we present a new and fast state-of-the-art Arabic diacritizer that guesses the diacritics of words and then their case endings.
Arabic POS Tagging: Don't Abandon Feature Engineering Just Yet
However, we show that augmenting bi-LSTM sequence labeling with some of the features that we used for the SVM-Rank based tagger yields to further improvements.
A Neural Architecture for Dialectal Arabic Segmentation
The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general.
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition
For language modelling, we made available over 110M words crawled from Aljazeera Arabic website Aljazeera. net for a 10 year duration 2000-2011.
Farasa: A New Fast and Accurate Arabic Word Segmenter
Meanwhile, Farasa is nearly one order of magnitude faster than QATARA and two orders of magnitude faster than MADAMIRA.
Arabic to English Person Name Transliteration using Twitter
We present a novel approach for mining data from Twitter for the purpose of building transliteration resources and systems.
Using Stem-Templates to Improve Arabic POS and Gender/Number Tagging
This paper presents an end-to-end automatic processing system for Arabic.
