United Arab Emirates

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.

EMNLP 2022

Identifying Code-switching in Arabizi

arabizi

language identification

corpora

code-switching

workshop paper

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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.

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.

Authorship Verification for Arabic Short Texts Using Arabic Knowledge-Base Model (AraKB)

In the online world, Machine Translation (MT) systems are extensively used to translate User-Generated Text (UGT) such as reviews, tweets, and social media posts, where the main message is often the author's positive or negative attitude towards the topic of the text. However, MT systems still lack accuracy in some low-resource languages and sometimes make critical translation errors that completely flip the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. This is particularly noticeable in texts that do not follow common lexico-grammatical standards such as the dialectical Arabic (DA) used on online platforms. In this research, we aim to improve the translation of sentiment in UGT written in the dialectical versions of the Arabic language to English. Given the scarcity of gold-standard parallel data for DA-EN in the UGT domain, we introduce a semi-supervised approach that exploits both monolingual and parallel data for training an NMT system initialised by a cross-lingual language model trained with  supervised and unsupervised modeling objectives. We assess the accuracy of sentiment translation by our proposed system through a numerical ‘sentiment-closeness' measure as well as human evaluation. We will show that our semi-supervised MT system can significantly help with correcting sentiment errors detected in the online translation of dialectical Arabic UGT.

A Semi-supervised Approach for a Better Translation of Sentiment in Dialectical Arabic UGT

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Deep learning algorithms can identify related tweets to reduce the information overload that prevents humanitarian organisations from using valuable Twitter posts. However, they rely heavily on human-labelled data, which are unavailable for emerging crises. Because each crisis has its own features, such as location, time and social media response, current models are known to suffer from generalising to unseen disaster events when pre-trained on past ones.Tweet classifiers for low-resource languages like Arabic has the additional issue of limited labelled data duplicates caused by the absence of good language resources. Thus, we propose a novel domain adaptation approach that employs distant supervision to automatically label tweets from emerging Arabic crisis events to be used to train a model along with available human-labelled data. We evaluate our work on data from seven 2018–2020 Arabic events from different crisis types (flood, explosion, virus and storm). Results show that our method outperforms self-training in identifying crisis-related tweets in real-time scenarios and can be seen as a robust Arabic tweet classifier.

Domain Adaptation for Arabic Crisis Response

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.

Event-Based Knowledge MLM for Arabic Event Detection

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

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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.

Identifying Code-switching in Arabizi

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Authorship Verification for Arabic Short Texts Using Arabic Knowledge-Base Model (AraKB)

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Identifying Code-switching in Arabizi

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Next from EMNLP 2022

Authorship Verification for Arabic Short Texts Using Arabic Knowledge-Base Model (AraKB)

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads