Malta

This study presents a strong methodology for detecting offensive content in multilingual text, with a focus on Kannada and Kannada-English mixed comments. The first step in data pre- processing is to work with a dataset containing Kannada comments, which is backed by Google Translate for Kannada-English translation. Following tokenization and sequence labeling, BIO tags are assigned to indicate the existence and bounds of objectionable spans within the text. On annotated data, a Bidirectional LSTM neural network model is trained and achieves an F1 score of 61.0 in recognizing objectionable content. Data preparation, model architecture definition, and iterative training with Kannada and Kannada-English text are all part of the training process. In a fresh dataset, the trained model accurately predicts offensive spans, emphasizing comments in the aforementioned languages. Predictions that have been recorded and include offensive span indices are organized into a database.

EACL 2024

Offensive Content Detection in Kannada and Kannada-English Mixed Text Using Deep Learning Techniques

and o for outside)bio tagging.

i for inside

(b stands for beginning

natural language processing(nlp)

bidirectional long short-term memory(bilstm)

offensive content detection

deep learning

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

Please register!

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Sentiment analysis has been an active field of research for over 20 years and has gained immense popularity due to its applications in both academia and industry. Sentiment Analysis of code-mixed posts and comments on social media, especially in Dravidian languages, is gaining more and more traction. This paper describes the team Transformers' submission to the Sentiment Analysis in Tamil shared task organized by DravidianLangTech 2024 workshop at EACL 2024. A BERT-based architecture, RoBERTa was used for the shared task. The best macro average F1-score achieved was 0.212. We secured the 5th rank in the Sentiment Analysis shared task in Tamil.

Transformers@DravidianLangTech-EACL2024: Sentiment Analysis of Code-Mixed Tamil Using RoBERTa

This research tackles the issue of fake news by utilizing the RNN-LSTM deep learning method with optimized hyperparameters identified through grid search. The model's performance in multi-label classification is hindered by unbalanced data, despite its success in binary classification. We achieved a score of 0.82 in the binary classification task, whereas in the multi-class task, the score was 0.32. We suggest incorporating data balancing techniques for researchers who aim to further this task, aiming to improve results in managing a variety of information.

Detecting Fake News Detection in Dravidian Languages using Deep Learning

The proliferation of fake news in digital media has become a significant societal concern, impacting public opinion, trust, and decision-making. This project focuses on the development of machine learning models for the detection of fake news. Leveraging a dataset containing both genuine and deceptive news articles, the proposed models employ natural language processing techniques, feature extraction and classification algorithms. This paper provides a solution to Fake News Detection in Dravidian Languages - DravidianLangTech@EACL 2024. There are two sub tasks: Task 1 -The goal of this task is to classify a given social media text into original or fake. We propose an approach for this with the help of a supervised machine learning model – SVM (Support Vector Machine). The SVM classifier achieved a macro F1 score of 0.78 in test data and a rank 11. The Task 2 is classifying fake news articles in Malayalam language into different categories namely False, Half True, Mostly False, Partly False and Mostly True.We have used Naive Bayes which achieved macro f1-score 0.3517 in test data and a rank 6.

Wordwizard Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024

Hate and offensive language in online platforms pose significant challenges, necessitating automatic detection methods. Particularly in the case of codemixed text, which is very common in social media, the complexity of this problem increases due to the cultural nuances of different languages. A shared task on the detection of hate and offensive language for Telugu was organized by DravidianLangTech. To complete this task, our study investigates the effectiveness of transliteration-augmented datasets for Telugu codemixed text. We compare the performance of various machine learning, deep learning, and transformer-based models on both original and augmented datasets. Our findings demonstrate the superiority of transformer models, particularly Telugu-BERT, which achieved an $f_1$ score of 0.77 on the augmented dataset, ranking the $1^{st}$ position in the leaderboard. The study highlights the potential of transliteration-augmented datasets in improving model performance and suggests further exploration of diverse transliteration options to address real-world scenarios.

Sandalphon@DravidianLangTech-EACL2024: Hate and Offensive Language Detection in Telugu Codemixed Text using Transliteration-Augmentation

Due to technological advancements, various methods have emerged for disseminating news to the masses. The pervasive reach of news, however, has given rise to a significant concern - the proliferation of fake news. In response to this challenge, a shared task in DravidianLangTech@EACL 2024 was initiated to detect fake news and classify its types in the Malayalam language. The shared task consisted of two sub-tasks. Task-1 focused on a binary classification problem, determining whether a piece of news is fake or not. Whereas task-2 delved into a multi-class classification problem, categorizing news into five distinct levels. Our approach involved the exploration of various machine learning, deep learning, and transformer models, with an emphasis on parameter tuning to enhance overall model performance. As a result, we introduce a MuRIL model that leverages parameter tuning, achieving notable success with an F1-score of 0.86 in task-1 and 0.5191 in task-2. This successful implementation led to our system securing the 3rd position in task-1 and the 1st position in task-2. The source code will be found in the GitHub repository provided in this link: https://github.com/Salman1804102/DravidianLangTech-EACL-2024-FakeNews.

CUET_Binary_Hackers@DravidianLangTech@EACL 2024: Malayalam Fake News Detection by Fine-Tuned MuRIL BERT

Fake news on social media is increasingly used to target and manipulate public opinion, influencing the decisions of a large number of people beyond mere misinformation. Researchers are increasingly interested in verifying news legitimacy due to the difficulty and cost of manual verification, especially with the increasing use of social media and internet access. Unfortunately, research on low-resource languages like Malayalam remains negligible due to a lack of resources and language processing tools. To address the issue the paper describes the submission of the team Punny Punctuators to the shared task on Fake News Detection in Dravidian Languages-DravidianLangTech@EACL 2024. The shared task consists of two sub-tasks: (i) the first subtask classifies a given social media text as original or fake, and (ii) the second subtask classifies fake news into different categories. We used many transformer-based, deep-learning, and machine-learning models. The Malayalam-BERT model performs better than the other methods in subtask-1 and subtask-2, placing it 2nd with a macro F1-score of 0.87 and 11th with a macro F1-score of 0.17, respectively.

Punny_Punctuators@DravidianLangTech-EACL2024: Detection and Classification of Fake News for Malayalam Social-Media Text

In this modern era, many people have been using Facebook and Twitter, leading to a rise in information sharing and communication. However, a considerable amount of information on these platforms is misleading or intentionally crafted to deceive users, which is often termed "Fake News". This shared task on Fake News Detection in Dravidian Languages (Task-1) organized by DravidianLangTech@EACL 2024 allows for addressing the challenge of distinguishing between original and fake news content in the Malayalam language. Our approach involves creating an intelligent model to categorize text as either fake or original. We experimented with various models, including Logistic Regression, Decision Tree, Random Forest, SVM, and SGD for machine learning; CNN, BiLSTM, and BiLSTM with attention to deep learning; and XLM-R and m-BERT for transformer-based approaches. Notably, our most successful model, m-BERT, achieved a top-ranking position with a macro F1 score of 0.85, ranked 4th among the participants. This research contributes to combating misinformation on social media news, offering an effective solution to classify content accurately.

CUET_NLP_GoodFellows@DravidianLangTech 2024: A transformer-based approach for detecting Fake News in Dravidian Languages

With the continuous evolution of technology and widespread internet access, various social media platforms have gained immense popularity, attracting a vast number of active users globally. However, this surge in online activity has also led to a concerning trend by driving many individuals to resort to posting hateful and offensive comments or posts, publicly targeting groups or individuals. In response to these challenges, we participated in this shared task. Our approach involved proposing a fine-tuning-based pre-trained transformer model to effectively discern whether a given text contains offensive content that propagates hatred. We conducted comprehensive experiments, exploring various machine learning (LR, SVM, and Ensemble), deep learning (CNN, BiLSTM, CNN+BiLSTM), and transformer models (indic-SBERT, mBERT, MuRIL, Distil-mBERT, XLMR), adhering to a meticulous fine-tuning methodology. Among the models evaluated, our fine-tuned L3Cube-Indic-Sentence-Similarity-BERT or Indic-SBERT model demonstrated superior performance, achieving a macro-average F1-score of 0.7013. This notable result positioned us at the 6th place in the task. The implementation details of the task will be found in the GitHub repository.

CUET_Binary_Hackers@DravidianLangTech@EACL 2024: Hate and Offensive Language Detection in Telugu Code-Mixed Text Using Sentence Similarity BERT

The ever-evolving landscape of online social media has initiated a transformative phase in communication, presenting unprecedented opportunities alongside inherent challenges. The pervasive issue of false information, commonly termed fake news, has emerged as a significant concern within these dynamic platforms. This study delves into the domain of Fake News Detection, with a specific focus on Malayalam. Utilizing advanced transformer models like mBERT, ALBERT, and XMLRoBERTa, our research proficiently classifies social media text into original or fake categories. Notably, our proposed model achieved commendable results, securing a rank of 3 in Task 1 with macro F1 scores of 0.84 using mBERT, 0.56 using ALBERT, and 0.84 using XMLRoBERTa. In Task 2, the XMLRoBERTa model excelled with a rank of 12, attaining a macro F1 score of 0.21, while mBERT and BERT achieved scores of 0.16 and 0.11, respectively. This research aims to develop robust systems capable of discerning authentic from deceptive content, a crucial endeavor in maintaining information reliability on social media platforms amid the rampant spread of misinformation.

TechWhiz@DravidianLangTech 2024: Fake News Detection Using Deep Learning Models

Sentiment analysis delves into people's opinions, intuitions, and emotions regarding any entity. Within natural language processing (NLP), it serves as a technique to extract subjective knowledge, determining whether an opinion or comment leans positive, negative, neutral, or a mix thereof towards an entity. In recent years, it has garnered substantial attention from NLP researchers due to the vast availability of comments and opinions on the internet. Despite extensive studies in this domain, sentiment analysis in low-resource languages such as Tamil and Tulu faces challenges in handling code-mixed and transliterated content. To address these challenges, this work focuses on sentiment analysis of code-mixed and transliterated Tamil and Tulu social media comments. It explores four machine learning approaches (LR, SVM, XGBoost, Ensemble), two deep learning methods (BiLSTM with fastText and Word2Vec), and four transformer-based models (m-BERT, MuRIL, L3Cube-IndicSBERT, and DistilmBERT) for both languages. For Tamil, L3Cube-IndicSBERT and ensemble approaches outperform others, while m-BERT demonstrates superior performance among the models for Tulu. The presented models achieved the third and first ranks by attaining macro F1-scores of 0.227 and 0.584 in Tamil and Tulu, respectively.

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