United Arab Emirates

This paper describes team GMU-WLV submission to the TSAR shared-task on multilingual lexical simplification. The goal of the task is to automatically provide a set of candidate substitutions for complex words in context. The organizers provided participants with ALEXSIS a manually annotated dataset with instances split between a small trial set with a dozen instances in each of the three languages of the competition (English, Portuguese, Spanish) and a test set with over 300 instances in the three aforementioned languages. To cope with the lack of training data, participants had to either use alternative data sources or pre-trained language models. We experimented with monolingual models: BERTimbau, ELECTRA, and RoBERTA-largeBNE. Our best system achieved 1st place out of sixteen systems for Portuguese, 8th out of thirty-three systems for English, and 6th out of twelve systems for Spanish.

EMNLP 2022

GMU-WLV at TSAR-2022 Shared Task: Evaluating Lexical Simplification Models

machine learning

text simplification

readability

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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.

Most existing proposals about anaphoric zero pronoun (AZP) resolution regard full mention coreference and AZP resolution as two independent tasks, even though the two tasks are clearly related. The main issues that need tackling to develop a joint model for zero and non-zero mentions are the difference between the two types of arguments (zero pronouns, being null, provide no nominal information) and the lack of annotated datasets of a suitable size in which both types of arguments are annotated for languages other than Chinese and Japanese. In this paper, we introduce two architectures for jointly resolving AZPs and non-AZPs, and evaluate them on Arabic, a language for which, as far as we know, there has been no prior work on joint resolution. Doing this also required creating a new version of the Arabic subset of the standard coreference resolution dataset used for the CoNLL-2012 shared task (Pradhan et al.,2012) in which both zeros and non-zeros are included in a single dataset.

Joint Coreference Resolution for Zeros and non-Zeros in Arabic

Contrastive learning (CL) has brought significant progress to various NLP tasks. Despite such a progress, CL has not been applied to Arabic NLP. Nor is it clear how much benefits it could bring to particular classes of tasks such as social meaning (e.g., sentiment analysis, dialect identification, hate speech detection). In this work, we present a comprehensive benchmark study of state-of-the-art supervised CL methods on a wide array of Arabic social meaning tasks. Through an extensive empirical analysis, we show that CL methods outperform vanilla finetuning on most of the tasks. We also show that CL can be data efficient and quantify this efficiency, demonstrating the promise of these methods in low-resource settings vis-a-vis the particular downstream tasks (especially label granularity).

A Benchmark Study of Contrastive Learning for Arabic Social Meaning

In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop. The task of gender rewriting refers to generating alternatives of a given sentence to match different target user gender contexts (e.g., a female speaker with a male listener, a male speaker with a male listener, etc.). This requires changing the grammatical gender (masculine or feminine) of certain words referring to the users. In this task, we focus on Arabic, a gender-marking morphologically rich language. A total of five teams from four countries participated in the shared task.

The Shared Task on Gender Rewriting

Propaganda is defined as an expression of opinion or action by individuals or groups deliberately designed to influence opinions or actions of other individuals or groups with reference to predetermined ends and this is achieved by means of well-defined rhetorical and psychological devices. Currently, propaganda (or persuasion) techniques have been commonly used on social media to manipulate or mislead social media users. Automatic detection of propaganda techniques from textual, visual, or multimodal content has been studied recently, however, major of such efforts are focused on English language content. In this paper, we propose a shared task on detecting propaganda techniques for Arabic textual content. We have done a pilot annotation of 200 Arabic tweets, which we plan to extend to 2,000 tweets, covering diverse topics.  We hope that the shared task will help in building a community for Arabic propaganda detection. The dataset will be made publicly available, which can help in future studies.

Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic

We present our work on collecting ArzEn-ST, a code-switched Egyptian Arabic-English Speech Translation Corpus. This corpus is an extension of the ArzEn speech corpus, which was collected through informal interviews with bilingual speakers. In this work, we collect translations in both directions, monolingual Egyptian Arabic and monolingual English, forming a three-way speech translation corpus. We make the translation guidelines and corpus publicly available. We also report results for baseline systems for machine translation and speech translation tasks. We believe this is a valuable resource that can motivate and facilitate further research studying the code-switching phenomenon from a linguistic perspective and can be used to train and evaluate NLP systems.

ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic-English

We present Maknuune, a large open lexicon for the Palestinian Arabic dialect. Maknuune  has over 36K entries from 17K lemmas, and 3.7K roots. All entries include diacritized Arabic orthography, phonological transcription and English glosses. Some entries are enriched with additional information such as broken plurals and templatic feminine forms, associated phrases and collocations, Standard Arabic glosses, and examples or notes on grammar, usage, or location of collected entry

Maknuune: A Large Open Palestinian Arabic Lexicon

This paper sheds light on an in-progress work for building a morphological analyzer for Egyptian Arabic (EGY). To build such a tool, a tag-set schema is developed depending on a corpus of 527,000 EGY words covering different sources and genres. This tag-set schema is used in annotating about 318,940 words, morphologically, according to their contexts. Each annotated word is associated with its suitable prefix(s), original stem, tag, suffix(s), glossary, number, gender, definiteness, and conventional lemma and stem. These morphologically annotated words, in turns, are used in developing the proposed morphological analyzer where the morphological lexicons and the compatibility tables are extracted and tested. The system is compared with one of best EGY morphological analyzers; CALIMA.

Developing a Tag-Set and Extracting the Morphological Lexicons to Build a Morphological Analyzer for Egyptian Arabic

Despite the noticeable progress that we recently witnessed in Arabic pre-trained language models (PLMs), the linguistic knowledge captured by these models remains unclear. In this paper, we conducted a study to evaluate available Arabic PLMs in terms of their linguistic knowledge. BERT-based language models (LMs) are evaluated using Minimum Pairs (MP), where each pair represents a grammatical sentence and its contradictory counterpart. MPs isolate specific linguistic knowledge to test the model's sensitivity in understanding a specific linguistic phenomenon. We cover nine major Arabic phenomena: Verbal sentences, Nominal sentences, Adjective Modification, and Idafa construction. The experiments compared the results of fifteen Arabic BERT-based PLMs. Overall, among all tested models, CAMeL-CA outperformed the other PLMs by achieving the highest overall accuracy.

Assessing the Linguistic Knowledge in Arabic Pre-trained Language Models Using Minimal Pairs

We describe a corpus of social media posts that include utterances in Arabizi, a Roman-script rendering of Arabic, mixed with other languages, notably English, French, and Arabic written in the Arabic script. We manually annotated a subset of the texts with word-level language IDs; this is a non-trivial task due to the nature of mixed-language writing, especially on social media. We developed classifiers that can accurately predict the language ID tags. Then, we extended the word-level predictions to identify sentences that include Arabizi (and code-switching), and applied the classifiers to the raw corpus, thereby harvesting a large number of additional instances. The result is a large-scale dataset of Arabizi, with precise indications of code-switching between Arabizi and English, French, and Arabic.

Identifying Code-switching in Arabizi

Arabic is a Semitic language, considered to be one of the most complex languages in the world due to its unique composition and complex linguistic features. It consequently causes challenges for verifying the authorship of Arabic texts, requiring extensive research and development. This paper presents a new knowledge-based model to enhance Natural Language Understanding and thereby improve authorship verification performance. The proposed model provided promising results that would benefit the Arabic research for different Natural Language Processing tasks.

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