United Arab Emirates

The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task demands the model to label the given text as having one or more types of propaganda techniques. There are a total of 21 propaganda techniques to be detected. We show that an ensemble of five models performs the best on the task, scoring a micro-F1 score of 59.73%. We also conduct comprehensive ablations and propose various future directions for this work.

EMNLP 2022

ChavanKane at WANLP 2022 Shared Task: Large Language Models for Multi-label Propaganda Detection

multilabel classification

sentiment analysis

natural language processing

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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.

Nowadays, the rapid dissemination of data on digital platforms has resulted in the emergence of information pollution and data contamination, specifically mis-information, mal-information, dis-information, fake news, and various types of propaganda. These topics are now posing a serious threat to the online digital realm, posing numerous challenges to social media platforms and governments around the world. In this article, we propose a propaganda detection model based on the transformer-based model AraBERT, with the objective of using this framework to detect propagandistic content in the Arabic social media text scene, well with purpose of making online Arabic news and media consumption healthier and safer. Given the dataset, our results are relatively encouraging, indicating a huge potential for this line of approaches in Arabic online news text NLP.

AraBERT Model for Propaganda Detection

In today's time, online users are regularly exposed to media posts that are propagandistic. Several strategies have been developed to promote safer media consumption in Arabic to combat this. However, there is a limited available multilabel annotated social media dataset. In this work, we have used a pre-trained AraBERT twitter-base model on an expanded train data via data augmentation. Our team CNLP-NITS-PP, has achieved the third rank in subtask 1 at WANLP-2022, for propaganda detection in Arabic (shared task) in terms of micro-F1 score of 0.602.

CNLP-NITS-PP at WANLP 2022 Shared Task: Propaganda Detection in Arabic using Data Augmentation and AraBERT Pre-trained Model

This paper presents the system developed by the NGU_CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks' two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.

NGU_CNLP atWANLP 2022 Shared Task: Propaganda Detection in Arabic

Transliteration is an important task in natural language processing (NLP) which aims to convert a name in the source language to the target language without changing its pronunciation. Particularly, transliteration from English to Arabic is highly needed in many applications, especially in countries (e.g., United Arab Emirates (UAE)) whose most citizens are foreigners but the official language is Arabic. In such a task-oriented scenario, namely transliterating the English names to the corresponding Arabic ones, the performance of the transliteration model is highly important. However, most existing neural approaches mainly apply a universal transliteration model with advanced encoders and decoders to the task, where limited attention is paid to leveraging the phonemic association between English and Arabic to further improve model performance. In this paper, we focus on transliteration of people's names from English to Arabic for the general public. In doing so, we collect a corpus named EANames by extracting high quality name pairs from online resources which better represent the names in the general public than linked Wikipedia entries that are always names of famous people). We propose a model for English-Arabic transliteration, where a memory module modeling the phonemic association between English and Arabic is used to guide the transliteration process. We run experiments on the collected data and the results demonstrate the effectiveness of our approach for English-Arabic transliteration.

Improving English-Arabic Transliteration with Phonemic Memories

Microaggressions are subtle manifestations of bias (Breitfeller et al., 2019). These demonstrations of bias can often be classified as a subset of abusive language. However, not as much focus has been placed on the recognition of these instances. In this study, we introduce an unsupervised method to detect microaggressions in natural language expressions. The algorithm relies on pre-trained word-embeddings, leveraging the bias encoded in the model in order to detect microaggressions in unseen textual instances. We test the method on a dataset of
racial and gender-based microaggressions, reporting promising results.

Leveraging Bias in Pre-trained Word Embeddings for Unsupervised Microaggression Detection

The major component of a news item that aids in giving a quick peek at the article's theme is the headline. The process of creating a headline involves summarizing an article to produce a summary that is sufficient in terms of clarity and consistency. Although many different types of research have been done in the domain of headline generation, it is still difficult to create an effective machine-generated title. Additionally, those methods do not work well for morphologically rich languages like Amharic. We proposed a neural headline generation model using transformers and different types of embedding techniques.

Transformer Based Amharic Headline Generation using Sub-word2Vec Representation

Building a question answering (QA) model with less annotation costs can be achieved by utilizing active learning (AL) training strategy. It selects the most informative unlabeled training data to update the model effectively. Acquisition functions for AL are used to determine how informative each training example is, such as uncertainty or diversity based sampling. In this work, we propose a perturbation-based active learning acquisition strategy and demonstrate it is more effective than existing commonly used strategies for the QA task.

Perturbation-based Active Learning for Question Answering

Identification of hate speech (HS) on social media platforms has become a Sisyphean task with the increment of web data availability and the human knack for discovering loopholes in the existing HS detection algorithms.This study conducts a comparative analysis of the merits of using two transformer approaches for HS detection: BART and RoBERTa. By considering popular benchmark data sets, we show that the BART outperforms RoBERTa for the monolingual classification of both finding hate and non-hate as well as classifying hate speech into categories.

Short Comparative Analysis on Pretrained BART and RoBERTa in
Detecting Hate Speech on YouTube and Reddit Platforms

These days, many people use social media as a source of information and medium of communication due to its easy to access, fast to disseminate and low cost platform. However, it also enables the wide propagation of fake news which causes economic, political, and social crises to the society. As a result, many researchers have been working towards detecting fake news. Most of the researches concerned on linguistic analysis of news content to identify its credibility, however fake news is also written intentionally to mislead users by mimicking true news. Beside this, Amharic is one of the under-resourced language that suffer from the benefits of fake news detection. To overcome the problem of fake news using content feature and under-resourced language, this study uses a feature fusion of linguistic and social context feature of the publisher information to detect Amharic fake news. The fusion-based experiment shows at least 94.13% and at most 98.7% relative error reduction over the content-based approaches.

Amharic Fake News Detection on Social Media Using Feature Fusion

The main goal of this work is to develop bi-directional machine translation between Amharic and Kistanigna. Due to an increase in the number of language users, to address the issues of the endangered of the Kistanigna language and to increases the content of the language in web, it is essential to develop machine translation between Kistanigna and Amharic language. In this study the translation is implemented using neural machine translation. We conduct the experiments using LSTM, Bi-LSTM, LSTM + attention, CNN + attention and Transformer encoder-decoder model. We considered training time, memory usage, and BLEU score when proposing an optimal model. Finally, we suggested the morpheme-based bidirectional machine translation using Transformer with BLEU scores of 21.31 and 22.40 for Amharic-Kistanigna and Kistanigna-Amharic translation respectively. The major weakness of the study is unavailability of enough dataset to conduct an extensive experiment. As a result, there is a need to prepare parallel corpora for conducting similar research.


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