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A Multilingual African Embedding for FAQ Chatbots

Searching for an available, reliable, official, and understandable information is not a trivial task due to scattered information across the internet, and the availability lack of governmental communication channels communicating with African dialects and languages...

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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. Our method for building this dataset relies on applying multiple fi...

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BERT Fine-tuning For Arabic Text Summarization

Fine-tuning a pretrained BERT model is the state of the art method for extractive/abstractive text summarization, in this paper we showcase how this fine-tuning method can be applied to the Arabic language to both construct the first documented model for abstractiv...

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CTAB: Corpus of Tunisian Arabizi

This dataset has been created between 2017 and 2021 to provide a textual resource that can be used to study the behaviors of Tunisian people in writing Tunisian Arabic (ISO 693-3: aeb) in Latin Script. This corpus is constituted from messages written using Tunisian...

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Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis

On various Social Media platforms, people tend to use the informal way to communicate, or write posts and comments: their local dialects. In Africa, more than 1500 dialects and languages exist. Particularly, Tunisians talk and write informally using Latin letters a...

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NLLB-CLIP -- train performant multilingual image retrieval model on a budget

Today, the exponential rise of large models developed by academic and industrial institutions with the help of massive computing resources raises the question of whether someone without access to such resources can make a valuable scientific contribution. To explor...

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NLLB: No Language Left Behind

No Language Left Behind (NLLB) is a first-of-its-kind, AI breakthrough project that open-sources models capable of delivering high-quality translations directly between any pair of 200+ languages — including low-resource languages like Asturian, Luganda, Urdu and m...

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No Language Left Behind : Data

NLLB project uses data from three sources : public bitext, mined bitext and data generated using backtranslation. Details of different datasets used and open source links are provided in details here.

No Language Left Behind: Scaling Human-Centered Machine Translation

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the va...

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QCRI Arabic Dialects Identification (QADI) Corpus

QCRI Arabic Dialects Identification (QADI) is a Country-level Arabic dialects identification (DI) dataset. It provides a collection for benchmarking DI task.The dataset contains 540,590 tweets from 18 Arab countries.

Qed Amara

The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform. Developed by: Qatar Computing Research ...

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Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments

Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment ...

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SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects

Despite the progress we have recorded in the last few years in multilingual natural language processing, evaluation is typically limited to a small set of languages with available datasets which excludes a large number of low-resource languages. In this paper, we c...

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The AMARA Corpus: Building Parallel Language Resources for the Educational Domain

This paper presents the AMARA corpus of on-line educational content: a new parallel corpus of educational video subtitles, multilingually aligned for 20 languages, i.e. 20 monolingual corpora and 190 parallel corpora. This corpus includes both resource-rich languag...

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The FLORES-200 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation

The creation of FLORES-200 doubles the existing language coverage of FLORES-101. Given the nature of the new languages, which have less standardization and require more specialized professional translations, the verification process became more complex. This requir...

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