United Arab Emirates

With the fast pace of reporting around the globe from various sources, event extraction has increasingly become an important task in NLP. The use of pre-trained language models (PTMs) has become popular to provide contextual representation for downstream tasks. This work aims to pre-train language models that enhance event extraction accuracy. To this end, we propose an Event-Based Knowledge (EBK) masking approach to mask the most significant terms in the event detection task. These significant terms are based on an external knowledge source that is curated for the purpose of event detection for the Arabic language. The proposed approach improves the classification accuracy of all the 9 event types. The experimental results demonstrate the effectiveness of the proposed masking approach and encourage further exploration.

EMNLP 2022

Event-Based Knowledge MLM for Arabic Event Detection

arabic dataset

event detection

language modeling

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

One core challenge facing morphological inflection systems is capturing language-specific morphophonological changes. This is particularly true of languages like Arabic which are morphologically complex. In this paper, we learn explicit morphophonological rules from morphologically annotated Egyptian Arabic and corresponding surface forms. These rules are human-interpretable, capture known morphophonological phenomena in the language, and are generalizable to unseen forms.

Towards Learning Arabic Morphophonology

Among mental health diseases, depression is one of the most severe, as it often leads to suicide which is the fourth leading cause of death in the Middle East. In the Middle East, Egypt has the highest percentage of suicidal deaths; due to this, it is important to identify depression and suicidal ideation.  In Arabic culture, there is a lack of awareness regarding the importance of diagnosing and living with mental health diseases. However, as noted for the last couple years people all over the world, including Arab citizens, tend to express their feelings openly on social media. Twitter is the most popular platform designed to enable the expression of emotions through short texts, pictures, or videos.  This paper aims to predict depression and depression with suicidal ideation.  Due to the tendency of people to treat social media as their personal diaries and share their deepest thoughts on social media platforms.  Social data contain valuable information that can be used to identify user's psychological states. We create AraDepSu dataset by scrapping tweets from twitter and manually labelling them.  We expand the diversity of user tweets, by adding a neutral label ("neutral”) so the dataset include three classes ("depressed”, "suicidal”, "neutral”). Then we train our AraDepSu dataset on 30+ different transformer models. We find that the best-performing model is MARBERT with accuracy, precision, recall and F1-Score values of 91.20%, 88.74%, 88.50% and 88.75%.

AraDepSu: Detecting Depression and Suicidal Ideation in Arabic Tweets Using Transformers

Speech translation (ST) is the task of directly translating acoustic speech signals in a source language into text in a foreign language. ST task has been addressed, for a long time, using a pipeline approach with two modules : first an Automatic Speech Recognition (ASR) in the source language followed by a text-to-text Machine translation (MT). In the past few years, we have seen a paradigm shift towards the end-to-end approaches using sequence-to-sequence deep neural network models. This paper presents our efforts towards the development of the first Broadcast News end-to-end Arabic to English speech translation system. Starting from independent ASR and MT LDC releases, we were able to identify about 92 hours of Arabic audio recordings for which the manual transcription was also translated into English at the segment level. These data was used to train and compare pipeline and end-to-end speech translation systems under multiple scenarios including transfer learning and data augmentation techniques.

End-to-End Speech Translation of Arabic to English Broadcast News

Emoji (digital pictograms) are crucial features for textual sentiment analysis. However, analysing the sentiment roles of emoji is very complex. This is due to its dependency on different factors, such as textual context, cultural perspective, interlocutor's personal traits,  interlocutors' relationships or a platforms' functional features. This work introduces an approach to analysing the sentiment effects of emoji as textual features. Using an Arabic dataset as a benchmark, our results confirm the borrowed argument that each emoji has three different norms of sentiment role (negative, neutral or positive). Therefore, an emoji can play different sentiment roles depending upon the context. It can behave as an emphasizer, an indicator, a mitigator, a reverser or a trigger of either negative or positive sentiment within a text. In addition, an emoji may have a neutral effect (i.e., no effect) on the sentiment of the text.

Emoji Sentiment Roles for Sentiment Analysis: A Case Study in Arabic Texts

Arabic diacritic recovery is important for a variety of downstream tasks such as text-to-speech. In this paper, we introduce a new Gulf Arabic diacritization dataset composed of 19,850 words based on a subset of the Gumar corpus.  We provide comprehensive set of guidelines for diacritization to enable the diacritization of more data.  We also report on diacritization results based on the new corpus using a Hidden Markov Model and character-based sequence to sequence models.

Gulf Arabic Diacritization: Guidelines, Initial Dataset, and Results

Wikipedia is a common source of training data for Natural Language Processing (NLP) research, especially as a source for corpora in languages other than English. However, for many downstream NLP tasks, it is important to understand the degree to which these corpora reflect representative contributions of native speakers. In particular, many entries in a given language may be translated from other languages or produced through other automated mechanisms. Language models built using corpora like Wikipedia can embed history, culture, bias, stereotypes, politics, and more, but it is important to understand whose views are actually being represented. In this paper, we present a case study focusing specifically on differences among the Arabic Wikipedia editions (Modern Standard Arabic, Egyptian, and Moroccan). In particular, we document issues in the Egyptian Arabic Wikipedia with automatic creation/generation and translation of content pages from English without human supervision. These issues could substantially affect the performance and accuracy of Large Language Models (LLMs) trained from these corpora, producing models that lack the cultural richness and meaningful representation of native speakers. Fortunately, the metadata maintained by Wikipedia provides visibility into these issues, but unfortunately, this is not the case for all corpora used to train LLMs.

Learning From Arabic Corpora But Not Always From Arabic Speakers: A Case Study of the Arabic Wikipedia Editions

This paper presents an open-source software library that provides a set of finite-state transducer (FST) components and corresponding utilities for manipulating the writing systems of languages that use the Perso-Arabic script. The operations include various levels of script normalization, including visual invariance-preserving operations that subsume and go beyond the standard Unicode normalization forms, as well as transformations that modify the visual appearance of characters in accordance with the regional orthographies for eleven contemporary languages from diverse language families. The library also provides simple FST-based romanization and transliteration. We additionally attempt to formalize the typology of Perso-Arabic characters by providing one-to-many mappings from Unicode code points to the languages that use them. While our work focuses on the Arabic script diaspora rather than Arabic itself, this approach could be adopted for any language that uses the Arabic script, thus providing a unified framework for treating a script family used by close to a billion people.

Beyond Arabic: Software for Perso-Arabic Script Manipulation

Coreference resolution is a key aspect of text comprehension, but the size of the available coreference corpora for Arabic is limited in comparison to the size of the corpora for other languages. In this paper we present a Game-With-A-Purpose called Stroll with a Scroll created to collect from players coreference annotations for Arabic. The key contribution of this work is the embedding of the annotation task in a virtual world setting, as opposed to the puzzle-type games used in previously proposed Games-With-A-Purpose for coreference.

Coreference Annotation of an Arabic Corpus using a Virtual World Game

This article describes the language identification system used by the SUKI team in the 2022 Nuanced Arabic Dialect Identification (NADI) shared task. In addition to the system description, we give some details of the dialect identification experiments we conducted while preparing our submissions. In the end, we submitted only one official run. We used a Naive Bayes-based language identifier with character n-grams from one to four, of which we implemented a new version, which automatically optimizes its parameters. We also experimented with clustering the training data according to different topics. With the macro F1 score of 0.1963 on test set A and 0.1058 on test set B, we achieved the 18th position out of the 19 competing teams.

Optimizing Naive Bayes for Arabic Dialect Identification

We describe our submitted system to the Nuanced Arabic Dialect Identification (NADI) shared task. We tackled only the first subtask (Subtask 1). We used state-of-the-art Deep Learning models and pre-trained contextualized text representation models that we finetuned according to the downstream task in hand. As a first approach, we used BERT Arabic variants: MARBERT with its two versions MARBERT v1 and MARBERT v2, we combined MARBERT embeddings with a CNN classifier, and finally, we tested the Quasi-Recurrent Neural Networks (QRNN) model. The results found show that version 2 of MARBERT outperforms all of the previously mentioned models on Subtask 1.

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Towards Learning Arabic Morphophonology

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