pwc-archive/papers-with-abstracts huggingface dataset:
Kawarith: an Arabic Twitter Corpus for Crisis Events
Authors: Alaa Alharbi, Mark Lee
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4

Social media (SM) platforms such as Twitter provide large quantities of real-time data that can be leveraged during mass emergencies. Developing tools to support crisis-affected communities requires available datasets, which often do not exist for low resource languages. This paper introduces Kawarith a multi-dialect Arabic Twitter corpus for crisis events, comprising more than a million Arabic tweets collected during 22 crises that occurred between 2018 and 2020 and involved several types of hazard. Exploration of this content revealed the most discussed topics and information types, and the paper presents a labelled dataset from seven emergency events that serves as a gold standard for several tasks in crisis informatics research. Using annotated data from the same event, a BERT model is fine-tuned to classify tweets into different categories in the multi- label setting. Results show that BERT-based models yield good performance on this task even with small amounts of task-specific training data.
Arabic Compact Language Modelling for Resource Limited Devices
Authors: Zaid Alyafeai, Irfan Ahmad
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Language Modeling, Language Modelling, Large Language Model, Quantization

Natural language modelling has gained a lot of interest recently. The current state-of-the-art results are achieved by first training a very large language model and then fine-tuning it on multiple tasks. However, there is little work on smaller more compact language models for resource-limited devices or applications. Not to mention, how to efficiently train such models for a low-resource language like Arabic. In this paper, we investigate how such models can be trained in a compact way for Arabic. We also show how distillation and quantization can be applied to create even smaller models. Our experiments show that our largest model which is 2x smaller than the baseline can achieve better results on multiple tasks with 2x less data for pretraining.
Arabic Emoji Sentiment Lexicon (Arab-ESL): A Comparison between Arabic and European Emoji Sentiment Lexicons
Authors: Shatha Ali A. Hakami, Robert Hendley, Phillip Smith
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Cultural Vocal Bursts Intensity Prediction, Sentiment Analysis

Emoji (the popular digital pictograms) are sometimes seen as a new kind of artificial and universally usable and consistent writing code. In spite of their assumed universality, there is some evidence that the sense of an emoji, specifically in regard to sentiment, may change from language to language and culture to culture. This paper investigates whether contextual emoji sentiment analysis is consistent across Arabic and European languages. To conduct this investigation, we, first, created the Arabic emoji sentiment lexicon (Arab-ESL). Then, we exploited an existing European emoji sentiment lexicon to compare the sentiment conveyed in each of the two families of language and culture (Arabic and European). The results show that the pairwise correlation between the two lexicons is consistent for emoji that represent, for instance, hearts, facial expressions, and body language. However, for a subset of emoji (those that represent objects, nature, symbols, and some human activities), there are large differences in the sentiment conveyed. More interestingly, an extremely high level of inconsistency has been shown with food emoji.
Dynamic Ensembles in Named Entity Recognition for Historical Arabic Texts
Authors: Muhammad Majadly, Tomer Sagi
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The use of Named Entity Recognition (NER) over archaic Arabic texts is steadily increasing. However, most tools have been either developed for modern English or trained over English language documents and are limited over historical Arabic text. Even Arabic NER tools are often trained on modern web-sourced text, making their fit for a historical task questionable. To mitigate historic Arabic NER resource scarcity, we propose a dynamic ensemble model utilizing several learners. The dynamic aspect is achieved by utilizing predictors and features over NER algorithm results that identify which have performed better on a specific task in real-time. We evaluate our approach against state-of-the-art Arabic NER and static ensemble methods over a novel historical Arabic NER task we have created. Our results show that our approach improves upon the state-of-the-art and reaches a 0.8 F-score on this challenging task.
Adult Content Detection on Arabic Twitter: Analysis and Experiments
Authors: Hamdy Mubarak, Sabit Hassan, Ahmed Abdelali
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4

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. In this paper, we present a dataset of Twitter accounts that post adult content. We perform an in-depth analysis of the nature of this data and contrast it with normal tweet content. Additionally, we present extensive experiments with traditional machine learning models, deep neural networks and contextual embeddings to identify such accounts. We show that from user information alone, we can identify such accounts with F1 score of 94.7% (macro average). With the addition of only one tweet as input, the F1 score rises to 96.8%.
UL2C: Mapping User Locations to Countries on Arabic Twitter
Authors: Hamdy Mubarak, Sabit Hassan
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Author Profiling, Dialect Identification

Mapping user locations to countries can be useful for many applications such as dialect identification, author profiling, recommendation system, etc. Twitter allows users to declare their locations as free text, and these user-declared locations are often noisy and hard to decipher automatically. In this paper, we present the largest manually labeled dataset for mapping user locations on Arabic Twitter to their corresponding countries. We build effective machine learning models that can automate this mapping with significantly better efficiency compared to libraries such as geopy. We also show that our dataset is more effective than data extracted from GeoNames geographical database in this task as the latter covers only locations written in formal ways.
ALUE: Arabic Language Understanding Evaluation
Authors: Haitham Seelawi, Ibraheem Tuffaha, Mahmoud Gzawi, Wael Farhan, Bashar Talafha, Riham Badawi, Zyad Sober, Oday Al-Dweik, Abed Alhakim Freihat, Hussein Al-Natsheh
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Diagnostic, Fairness, Multi-Task Learning

The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU). We strongly believe thatmany NLU problems in Arabic are especiallypoised to reap the benefits of such models. Tothis end we propose the Arabic Language Un-derstanding Evaluation Benchmark (ALUE),based on 8 carefully selected and previouslypublished tasks. For five of these, we providenew privately held evaluation datasets to en-sure the fairness and validity of our benchmark.We also provide a diagnostic dataset to helpresearchers probe the inner workings of theirmodels.Our initial experiments show thatMTL models outperform their singly trainedcounterparts on most tasks. But in order to en-tice participation from the wider community,we stick to publishing singly trained baselinesonly. Nonetheless, our analysis reveals thatthere is plenty of room for improvement inArabic NLU. We hope that ALUE will playa part in helping our community realize someof these improvements. Interested researchersare invited to submit their results to our online,and publicly accessible leaderboard.
Quranic Verses Semantic Relatedness Using AraBERT
Authors: Abdullah Alsaleh, Eric Atwell, Abdulrahman Altahhan
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Language Modeling, Language Modelling

Bidirectional Encoder Representations from Transformers (BERT) has gained popularity in recent years producing state-of-the-art performances across Natural Language Processing tasks. In this paper, we used AraBERT language model to classify pairs of verses provided by the QurSim dataset to either be semantically related or not. We have pre-processed The QurSim dataset and formed three datasets for comparisons. Also, we have used both versions of AraBERT, which are AraBERTv02 and AraBERTv2, to recognise which version performs the best with the given datasets. The best results was AraBERTv02 with 92% accuracy score using a dataset comprised of label ‘2’ and label '-1’, the latter was generated outside of QurSim dataset.
QuranTree.jl: A Julia Package for Quranic Arabic Corpus
Authors: Al-Ahmadgaid Asaad
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Transliteration

QuranTree.jl is an open-source package for working with the Quranic Arabic Corpus (Dukes and Habash, 2010). It aims to provide Julia APIs as an alternative to the Java APIs of JQuranTree. QuranTree.jl currently offers functionalities for intuitive indexing of chapters, verses, words and parts of words of the Qur’an; for creating custom transliteration; for character dediacritization and normalization; and, for handling the morphological features. Lastly, it can work well with Julia’s TextAnalysis.jl and Python’s CAMeL Tools.
SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs
Authors: Saher Esmeir
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Entity Embeddings, Entity Retrieval, Knowledge Graphs, Retrieval

Knowledge graphs (KGs) are widely used to store and access information about entities and their relationships. Given a query, the task of entity retrieval from a KG aims at presenting a ranked list of entities relevant to the query. Lately, an increasing number of models for entity retrieval have shown a significant improvement over traditional methods. These models, however, were developed for English KGs. In this work, we build on one such system, named KEWER, to propose SERAG (Semantic Entity Retrieval from Arabic knowledge Graphs). Like KEWER, SERAG uses random walks to generate entity embeddings. DBpedia-Entity v2 is considered the standard test collection for entity retrieval. We discuss the challenges of using it for non-English languages in general and Arabic in particular. We provide an Arabic version of this standard collection, and use it to evaluate SERAG. SERAG is shown to significantly outperform the popular BM25 model thanks to its multi-hop reasoning.
Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis
Authors: Chayma Fourati, Hatem Haddad, Abir Messaoudi, Moez BenHajhmida, Aymen Ben Elhaj Mabrouk, Malek Naski
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Language Modeling, Language Modelling, 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 and numbers rather than Arabic ones. In this paper, we introduce a large common-crawl-based Tunisian Arabizi dialectal dataset dedicated for Sentiment Analysis. The dataset consists of a total of 100k comments (about movies, politic, sport, etc.) annotated manually by Tunisian native speakers as Positive, negative and Neutral. We evaluate our dataset on sentiment analysis task using the Bidirectional Encoder Representations from Transformers (BERT) as a contextual language model in its multilingual version (mBERT) as an embedding technique then combining mBERT with Convolutional Neural Network (CNN) as classifier. The dataset is publicly available.
AraFacts: The First Large Arabic Dataset of Naturally Occurring Claims
Authors: Zien Sheikh Ali, Watheq Mansour, Tamer Elsayed, Abdulaziz Al‐Ali
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Fact Checking

We introduce AraFacts, the first large Arabic dataset of naturally occurring claims collected from 5 Arabic fact-checking websites, e.g., Fatabyyano and Misbar, and covering claims since 2016. Our dataset consists of 6,121 claims along with their factual labels and additional metadata, such as fact-checking article content, topical category, and links to posts or Web pages spreading the claim. Since the data is obtained from various fact-checking websites, we standardize the original claim labels to provide a unified label rating for all claims. Moreover, we provide revealing dataset statistics and motivate its use by suggesting possible research applications. The dataset is made publicly available for the research community.
Improving Cross-Lingual Transfer for Event Argument Extraction with Language-Universal Sentence Structures
Authors: Minh Van Nguyen, Thien Huu Nguyen
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Cross-Lingual Transfer, Event Argument Extraction, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Sentence

We study the problem of Cross-lingual Event Argument Extraction (CEAE). The task aims to predict argument roles of entity mentions for events in text, whose language is different from the language that a predictive model has been trained on. Previous work on CEAE has shown the cross-lingual benefits of universal dependency trees in capturing shared syntactic structures of sentences across languages. In particular, this work exploits the existence of the syntactic connections between the words in the dependency trees as the anchor knowledge to transfer the representation learning across languages for CEAE models (i.e., via graph convolutional neural networks – GCNs). In this paper, we introduce two novel sources of language-independent information for CEAE models based on the semantic similarity and the universal dependency relations of the word pairs in different languages. We propose to use the two sources of information to produce shared sentence structures to bridge the gap between languages and improve the cross-lingual performance of the CEAE models. Extensive experiments are conducted with Arabic, Chinese, and English to demonstrate the effectiveness of the proposed method for CEAE.
Country-level Arabic Dialect Identification Using Small Datasets with Integrated Machine Learning Techniques and Deep Learning Models
Authors: Maha J. Althobaiti
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: BIG-bench Machine Learning, Dialect Identification, Word Embeddings

Arabic is characterised by a considerable number of varieties including spoken dialects. In this paper, we presented our models developed to participate in the NADI subtask 1.2 that requires building a system to distinguish between 21 country-level dialects. We investigated several classical machine learning approaches and deep learning models using small datasets. We examined an integration technique between two machine learning approaches. Additionally, we created dictionaries automatically based on Pointwise Mutual Information and labelled datasets, which enriched the feature space when training models. A semi-supervised learning approach was also examined and compared to other methods that exploit large unlabelled datasets, such as building pre-trained word embeddings. Our winning model was the Support Vector Machine with dictionary-based features and Pointwise Mutual Information values, achieving an 18.94% macros-average F1-score.
BERT-based Multi-Task Model for Country and Province Level MSA and Dialectal Arabic Identification
Authors: Abdellah El Mekki, Abdelkader El Mahdaouy, Kabil Essefar, Nabil El Mamoun, Ismail Berrada, Ahmed Khoumsi
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Language Identification, Multi-Task Learning

Dialect and standard language identification are crucial tasks for many Arabic natural language processing applications. In this paper, we present our deep learning-based system, submitted to the second NADI shared task for country-level and province-level identification of Modern Standard Arabic (MSA) and Dialectal Arabic (DA). The system is based on an end-to-end deep Multi-Task Learning (MTL) model to tackle both country-level and province-level MSA/DA identification. The latter MTL model consists of a shared Bidirectional Encoder Representation Transformers (BERT) encoder, two task-specific attention layers, and two classifiers. Our key idea is to leverage both the task-discriminative and the inter-task shared features for country and province MSA/DA identification. The obtained results show that our MTL model outperforms single-task models on most subtasks.
Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features
Authors: Elsayed Issa, Mohammed AlShakhori1, Reda Al-Bahrani, Gus Hahn-Powell
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Attribute, Dialect Identification, Feature Engineering

This work investigates the value of augmenting recurrent neural networks with feature engineering for the Second Nuanced Arabic Dialect Identification (NADI) Subtask 1.2: Country-level DA identification. We compare the performance of a simple word-level LSTM using pretrained embeddings with one enhanced using feature embeddings for engineered linguistic features. Our results show that the addition of explicit features to the LSTM is detrimental to performance. We attribute this performance loss to the bivalency of some linguistic items in some text, ubiquity of topics, and participant mobility.
Arabic Dialect Identification based on a Weighted Concatenation of TF-IDF Features
Authors: Mohamed Lichouri, Mourad Abbas, Khaled Lounnas, Besma Benaziz, Aicha Zitouni
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Dialect Identification, regression

In this paper, we analyze the impact of the weighted concatenation of TF-IDF features for the Arabic Dialect Identification task while we participated in the NADI2021 shared task. This study is performed for two subtasks: subtask 1.1 (country-level MSA) and subtask 1.2 (country-level DA) identification. The classifiers supporting our comparative study are Linear Support Vector Classification (LSVC), Linear Regression (LR), Perceptron, Stochastic Gradient Descent (SGD), Passive Aggressive (PA), Complement Naive Bayes (CNB), MutliLayer Perceptron (MLP), and RidgeClassifier. In the evaluation phase, our system gives F1 scores of 14.87% and 21.49%, for country-level MSA and DA identification respectively, which is very close to the average F1 scores achieved by the submitted systems and recorded for both subtasks (18.70% and 24.23%).
Machine Learning-Based Approach for Arabic Dialect Identification
Authors: Hamada Nayel, Ahmed Hassan, Mahmoud Sobhi, Ahmed El-Sawy
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: BIG-bench Machine Learning, Dialect Identification, regression

This paper describes our systems submitted to the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). Dialect identification is the task of automatically detecting the source variety of a given text or speech segment. There are four subtasks, two subtasks for country-level identification and the other two subtasks for province-level identification. The data in this task covers a total of 100 provinces from all 21 Arab countries and come from the Twitter domain. The proposed systems depend on five machine-learning approaches namely Complement Naïve Bayes, Support Vector Machine, Decision Tree, Logistic Regression and Random Forest Classifiers. F1 macro-averaged score of Naïve Bayes classifier outperformed all other classifiers for development and test data.
Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic
Authors: Ibrahim Abu Farha, Wajdi Zaghouani, Walid Magdy
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Sarcasm Detection, Sentiment Analysis

This paper provides an overview of the WANLP 2021 shared task on sarcasm and sentiment detection in Arabic. The shared task has two subtasks: sarcasm detection (subtask 1) and sentiment analysis (subtask 2). This shared task aims to promote and bring attention to Arabic sarcasm detection, which is crucial to improve the performance in other tasks such as sentiment analysis. The dataset used in this shared task, namely ArSarcasm-v2, consists of 15,548 tweets labelled for sarcasm, sentiment and dialect. We received 27 and 22 submissions for subtasks 1 and 2 respectively. Most of the approaches relied on using and fine-tuning pre-trained language models such as AraBERT and MARBERT. The top achieved results for the sarcasm detection and sentiment analysis tasks were 0.6225 F1-score and 0.748 F1-PN respectively.
WANLP 2021 Shared-Task: Towards Irony and Sentiment Detection in Arabic Tweets using Multi-headed-LSTM-CNN-GRU and MaRBERT
Authors: Reem Abdel-Salam
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4

Irony and Sentiment detection is important to understand people’s behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The task was based on the ArSarcasm-v2 dataset (Abu Farha et al., 2021). In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT (Abdul-Mageed et al., 2021) submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username “rematchka”. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT.
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation
Authors: Abeer Abuzayed, Hend Al-Khalifa
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Data Augmentation, Sarcasm Detection, Sentiment Analysis

In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021). The shared task consists of two sub-tasks: Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). Our experiments were based on fine-tuning seven BERT-based models with data augmentation to solve the imbalanced data problem. For both tasks, the MARBERT BERT-based model with data augmentation outperformed other models with an increase of the F-score by 15% for both tasks which shows the effectiveness of our approach.
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis
Authors: Abdullah I. Alharbi, Mark Lee
Date: 2021-04-01 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Multi-Task Learning, Natural Language Understanding, Sarcasm Detection, Sentiment Analysis, Word Embeddings

Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding. Sarcasm is a type of expression where the sentiment polarity is flipped by an interfering factor. In this study, we exploited this relationship to enhance both tasks by proposing a multi-task learning approach using a combination of static and contextualised embeddings. Our proposed system achieved the best result in the sarcasm detection subtask.