Spain

This paper presents the ArabicProcessors team’s deep learning system designed for the NADI 2020 Subtask 1 (country-level dialect identification) and Subtask 2 (province-level dialect identification). We used Arabic-Bert in combination with data augmentation and ensembling methods. Unlabeled data provided by task organizers (10 Million tweets) was split into multiple subparts, to which we applied semi-supervised learning method, and finally ran a specific ensembling process on the resulting models. This system ranked 3rd in Subtask 1 with 23.26% F1-score and 2nd in Subtask 2 with 5.75% F1-score.

COLING 2020

Arabic dialect identification: 
An Arabic-BERT model with data augmentation and ensembling strategy

ensembling

arabic

dialect

deep learning

data augmentation

bert

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

poster

We describe Faheem (adj. of understand), our submission to NADI (Nuanced
Arabic Dialect Identification) shared task. With so many Arabic dialects
being understudied due to the scarcity of the resources, the objective is to
identify the Arabic dialect used in the tweet, at the country-level. We propose
a machine learning approach where we utilize word-level n-gram (n=1-3) and
tf-idf features and feed them to six different classifiers. We train the system
using a data set of 21,000 tweets covering 21 Arab countries. Our top
performing classifiers are: Logistic Regression, Support Vector Machines, and
Multinomial Naive Bayes (MNB). We achieved our best result of macro-F1=
0.151 using the MNB classifier.

Faheem at NADI shared task: Identifying the dialect of Arabic tweet

In this paper, we present our work for the NADI Shared Task : Nu-anced Arabic Dialect Identification for Subtask-1: country-level dialect identification. We intro-duce a Reverse Translation Corpus Extension Systems (RTCES) to handle data imbalance along with reported results on several experimented approaches of word and document representations and different models architectures. The top scoring model was based on the Transformer-based Model for Arabic Language Understanding (AraBERT), with our modified extended corpus based on reverse translation of the given Arabic tweets. The selected system achieved a macro average F1 score of 20.34% on the test set, which places our team CodeLyoko as the 7th out of 18 teams in the final ranking Leaderboard.

Identifying Nuanced Dialect for Arabic Tweets with Deep Learning and Reverse Translation Corpus Extension System

We present an Arabic dialect identification system that we used for the country-level subtask of the NADI dataset. Our model consists of three components: BiLSTM-CNN, characterlevel TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieve an F1-Macro score of 20.77% and an accuracy of 34.32% on the test set. The model
was also evaluated on the Arabic Online Commentary dataset which focuses on the region-level dialects and MSA. We achieve results better than the state-of-the-art models.

The QMUL/HRBDT contribution to the NADI Arabic Dialect Identification Shared Task

Arabic dialects are among of three main variant of Arabic language (Classical Arabic, modern standard Arabic and dialectal Arabic). It has many variants according to the country, city (provinces) or town. In this paper, several techniques with multiple algorithms are applied for Arabic dialects identification starting from removing noise till classification task using all Arabic countries as 21 classes. Three types of classifiers (Naïve Bayes, Logistic Regression, and Decision Tree) are combined using voting with two different methodologies. Also clustering technique is used for decreasing the noise that result from the existing of MSA tweets in the data set for training phase. The results of f-measure were 27.17, 41.34 and 52.38 for first methodology without clustering, second methodology without clustering, and second methodology with clustering, the used data set is NADI shared task data set.

Arabic Dialects Identification for All Arabic countries

This paper presents our results for the Nuanced Arabic Dialect Identification (NADI) shared task of the Fifth Workshop for Arabic Natural Language Processing (WANLP 2020). We participated in the first sub-task for country-level Arabic dialect identification covering 21 Arab countries. Our contribution is based on a stacking classifier using Multinomial Naive Bayes, Linear SVC, and Logistic Regression classifiers as estimators; followed by a Logistic Regression as final estimator. Despite the fact that the results on the test set were low, with a macro F1 of 17.71, we were able to show that a simple approach can achieve comparable results to more sophisticated solutions. Moreover, the insights of our error analysis, and of the corpus content in general, can be used to develop and improve future systems.

Arabic Dialect Identification using a Stacking Classifier

Arabic, like other highly inflected languages, encodes a large amount of information in its morphology and word structure. In this work, we propose two embedding strategies that modify the tokenization phase of traditional word embedding models (Word2Vec) and contextual word embedding models (BERT) to take into account Arabic's relatively complex morphology. In Word2Vec, we segment words into subwords during training time and then compose word-level representations from the subwords during test time. We train our embeddings on Arabic Wikipedia and show that they perform better than a Word2Vec model on multiple Arabic natural language processing datasets while being approximately 60% smaller in size. Moreover, we showcase our embeddings' ability to produce accurate representations of some out-of-vocabulary words that were not encountered before. In BERT, we modify the tokenization layer of Google's pretrained multilingual BERT model by incorporating information on morphology. By doing so, we achieve state of the art performance on two Arabic NLP datasets without pretraining.

On the Importance of Tokenization in Arabic Embedding Models

We present our work on automatically detecting isnads, the chains of authorities for a report that serve as citations in hadith and other classical Arabic texts. We experiment with both sequence labeling methods for identifying isnads in a single pass and a hybrid “retrieve-and-tag” approach.

Tracing Traditions: Automatic Extraction of Isnads from Classical Arabic Texts

This research focuses on improving sequence labelling (SL) tasks by feeding neural networks with language specific characteristics in the form of embeddings.
Arabic is a morphologically rich language.
Arabic NLP researchers have used two types of approaches to work with Arabic text. 

Our Contribution: 
We propose a diacritic-aware architecture for SL which extends and outperforms the current standard character-aware architecture of a Bi-directional long short-term memory network (Bi-LSTM) with a CRF. 
We also propose a combination architecture for NER that combines word, character, diacritic, and POS information and outperforms the standard character-aware architecture and our own diacritic-aware architecture.





Embed More Ignore Less (EMIL): Exploiting Enriched Representations for Arabic NLP


Social media user-generated text is actually the main resource for many NLP tasks. This text however, does not follow the standard rules of writing. Moreover, the use of dialects such as Moroccan Arabic in written communications increases further NLP tasks complexity. A dialect is a verbal language that does not have a standard orthography, which leads users to improvise spelling while writing. Thus, for the same word we can find multiple forms of transliterations. Subsequently, it is mandatory to normalize these different transliterations to one canonical word form. To reach this goal, we have exploited the powerfulness of word embedding models generated with a corpus of YouTube comments. Besides, using a Moroccan Arabic dialect dictionary that provides the canonical forms, we have built a normalization dictionary that we refer to as MANorm . We have conducted several experiments to demonstrate the efficiency of MANorm, which have shown its usefulness in dialect normalization.

MANorm: A Normalization Dictionary for Moroccan Arabic Dialect Written in Latin Script

While online Arabic is primarily written using the Arabic script, a Roman-script variety called Arabizi is often seen on social media. Although this representation captures the phonology of the language, it is not a one-to-one mapping with the Arabic script version. This issue is exacerbated by the fact that Arabizi on social media is Dialectal Arabic which does not have a standard orthography. Furthermore, Arabizi tends to include a lot of code mixing between Arabic and English (or French). To map Arabizi text to Arabic script in the context of complete utterances, previously published efforts have split Arabizi detection and Arabic script target in two separate tasks. In this paper, we present the first effort on a unified model for Arabizi detection and transliteration into a code-mixed output with consistent Arabic spelling conventions, using a sequence-to-sequence deep learning model. Our best system achieves 80.6% word accuracy and 58.7% BLEU on a blind test set.

Downloads

Next from COLING 2020

Faheem at NADI shared task: Identifying the dialect of Arabic tweet

Similar lecture

Weighted combination of BERT and N-GRAM features for Nuanced Arabic Dialect Identification

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from COLING 2020

Faheem at NADI shared task: Identifying the dialect of Arabic tweet

Similar lecture

Weighted combination of BERT and N-GRAM features for Nuanced Arabic Dialect Identification

Downloads