Hate and offensive language detection is the task of detecting hate and/or offensive content targetting a person or a group of people. Despite many efforts to detect hate and offensive content on social media platforms, the problem remains unsolved till date due to the ever growing social media users and their creativity to create and spread hate and offensive content. To address the automatic detection of hate and offensive content on social media platforms, this paper describes the learning models submitted by our team - MUCS to "Hate and Offensive Language Detection in Telugu Codemixed Text (HOLD-Telugu): DravidianLangTech@EACL" - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024 invites the research community to address the challenges of detecting hate and offensive language in Telugu language. In this paper, we - team MUCS, describe the learning models submitted to the above mentioned shared task. Three models: Three models: i) LR model - a Machine Learning (ML) algorithm fed with TF-IDF of n-grams of subword, word and char_wb are in the range (1, 3), (1, 3), and (1, 5), ii) TL- a pretrained BERT models which makes use of Hate-speech-CNERG/bert-base-uncased-hatexplain model and iii) Ensemble model which is the combination of ML classifieres( MNB, LR, GNB) trained CountVectorizer with word and char ngrams of range (1, 3) and (1, 5) respectively. Proposed LR model trained with TF-IDF of subword, word and char n-grams outperformed the other models with macro F1 scores of 0.6501 securing 15th rank in the shared task for Telugu text.

MUCS@DravidianLangTech-2024: Role of Learning Approaches in Strengthening Hate-Alert Systems for Code-mixed Languages

Sentiment Analysis (SA) is a field of computational study that analyzes and understands people’s opinions, attitudes, and emotions toward any entity. A review of an entity can be written about an individual, an event, a topic, a product, etc., and such reviews are abundant on social media platforms. The increasing number of social media users and the growing amount of user-generated code-mixed content such as reviews, comments, posts etc., on social media have resulted in a rising demand for efficient tools capable of effectively analyzing such content to detect the sentiments. In spite of this, SA of social media text is challenging because the code-mixed text is complex. To address SA in code-mixed Tamil and Tulu text, this paper describes the Machine Learning (ML) models submitted by our team - MUCS to "Sentiment Analysis in Tamil and Tulu - Dravidian- LangTech" - a shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024. Linear Support Vector classifier (LinearSVC) and ensemble of 5 ML classifiers (k Nearest Neighbour (kNN), Stochastic Gradient Descent (SGD), Logistic Regression (LR), LinearSVC, and Random Forest Classifier (RFC)) with hard voting trained using concatenated features obtained from word and character n-ngrams vectoized from Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and CountVectorizer. Further, Gridsearch algorithm is employed to obtain optimal hyperparameter values.The proposed ensemble model obtained macro F1 scores of 0.260 and 0.550 for Tamil and Tulu languages respectively.

MUCS@DravidianLangTech-2024: A Grid Search Approach to explore Sentiment Analysis in Code-mixed Tamil and Tulu

Homophobic/Transphobic (H/T) content includes hate speeches, discriminatory texts directed at LGBT individuals. Furthermore, social media text data is often code-mixed, which is difficult to analyze, detect H/T content. To address this issue effectively, in this paper, we - team MUCS, describe the ML and TL based models submitted to "Homophobia/Transphobia Detection in social me- dia comments:LT-EDI@EACL 2024" shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. Three distinct models: i) Homo_Ensemble- a Machine Learning (ML) model trained with syllable TF-IDF of n= (1, 3), ii) Homo_TL - model makes use of oversampling technique used to fine-tune the Transfer Learning (TL) based pretrained Multilingual Bidirectional Encoder Representations from Transformers (mBERT) models, and iii) Homo_mvlearn - a multiview (mvlearn) model which ultizes the probability scores of two models. Among the proposed models Homo_Ensemble obtained macro F1 score of 0.958 securing 4th rank for Telugu, Homo_TL obtained macro F1 scores of 0.493, 0.537, 0.458, 0.948, 0.958 securing 2nd , 2nd , 1st , 1st , and 4th ranks for English, Marathi, Hindi, Kannada and Gujarathi languages respectively. The proposed Homo_mvlearn model obtained macro F1 scores of 0.860, 0.870, and 0.532 se- curing 2nd , 6th , and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively. Tulu Language task consists of Few-Shot Learning (FSL) problem. For the same, proposed two FSL models comprised of Sentence Transformer (ST) model with BERT pre-trained variant models namely, indic_sbert, kannada_sbert. Among these ST model with kannada_sbert ob- tained macro F1 score of 0.620 securing 2nd rank.

MUCS@LT-EDI-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification

Misogynistic memes are a category of memes which contain disrespectful language targeting women on social media platforms. Hence, detecting such memes is necessary in order to maintain a healthy social media environment. To address the challenges of detecting misogynistic memes, "Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes: LT-EDI@EACL 2024" shared task organized at European Chapter of the Association for Computational Linguistics (EACL) 2024, invites researchers to develop models to detect misogynistic memes in Tamil and Malayalam. The shared task has two subtasks and in this paper, we - team MUCS, describe the learning models submitted to Task 1 - Identification of Misogynistic Memes in Tamil and Malayalam. As memes represent multi-modal data of image and text, three models: i)~Bidirectional Encoder Representations from Transformers (BERT)+Residual Network (ResNet)-50, ii) Multilingual Representations for Indian Languages (MuRIL)+ResNet-50, and iii) multilingual BERT (mBERT)+ResNet-50, are proposed based on joint representation of text and image, for detecting misogynistic memes in Tamil and Malayalam. Among the proposed models, mBERT+ResNet-50 and MuRIL+ ResNet-50 models obtained macro F1 scores of 0.73 and 0.87 for Tamil and Malayalam datasets respectively securing 1st rank for both the datasets in the shared task.

MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification

[**Teleport to Gather**](https://app.gather.town/app/XDzSQW1b0mpTTAYr/EACL2024?spawnToken=XxkeWnX_QkG6G-llIfSi) The development of technology increases our internet use, and most of the global languages have adapted themselves to the digital era. However, there are many regional, under-resourced languages that face challenges as they still lack developments in language technology [1]. One such language family is the Dravidian family of languages. Dravidian languages are primarily spoken in south India and Sri Lanka. Pockets of speakers are found in Nepal, Pakistan, Malaysia, other parts of India and elsewhere in the world. The Dravidian languages, which are 4,500 years old [2] and spoken by millions of speakers, are under-resourced in speech and natural language processing [1]. The Dravidian languages are divided into four groups: South, South-Central, Central, and North groups. Dravidian morphology is agglutinating and exclusively suffixal. Syntactically, Dravidian languages are head-final and left-branching. They are free-constituent order languages. To improve access to and production of information for monolingual speakers of Dravidian languages, it is necessary to have speech and languages technologies. The aim of these workshops is to save the Dravidian languages from extinction in technology. This is the first workshop on speech and language technologies for Dravidian languages. 
 [**Read more**](https://sites.google.com/view/dravidianlangtech-2024/)
 **Program Overview** 
09:15 - 09:30 Opening Remarks 
09:30 - 10:00 Keynote 
10:00 - 11:00 Developing Models and Resources for Low Resource Applications - Chair: Bharathi Raja Chakravarthi, University of Galway, Ireland 
11:00 - 13:00 Poster Session - Chair: Prasanna Kumar Kumerasan, University of Galway, Ireland 
13:00 - 13:30 Lunch Break 
13:30 - 15:45 Overview and Findings of Shared Tasks at DravidianLangTech 2024 - Chair: Rahul Ponnusamy, University of Galway, Ireland 
15:45 - 17:00 Tea Break 
17:00 - 17:15 Meeting, Awards, Closing [**Detailed Program**](https://sites.google.com/view/dravidianlangtech-2024/schedule)

W19: The Fourth Workshop on Speech and Language Technologies for Dravidian Languages (DravidianLangTech)

poster

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!

EACL 2024

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.

[**Teleport to Gather**](https://app.gather.town/app/XDzSQW1b0mpTTAYr/EACL2024?spawnToken=QvCZ0RrQRM6hvHqVsTqW) Equality, Diversity and Inclusion (EDI) is an important agenda across every field throughout the world. Language as a major part of communication should be inclusive and treat everyone with equality. Today’s large internet community uses language technology (LT) and has a direct impact on people across the globe. EDI is crucial to ensure everyone is valued and included, so it is necessary to build LT that serves this purpose. Recent results have shown that big data and deep learning are entrenching existing biases and that some algorithms are even naturally biased due to problems such as ‘regression to the mode’. Our focus is on creating LT that will be more inclusive of gender, racial, sexual orientation, persons with disability. The workshop will focus on creating speech and language technology to address EDI not only in English, but also in less resourced languages. [**Read more**](https://sites.google.com/view/lt-edi-2024) [**Invited Speaker**](https://sites.google.com/view/lt-edi-2024/home) **Program Overview** 
09:00 - 09:15 Opening Remarks 
09:15 - 09:45 Keynote - Chair: Bharathi Raja Chakravarthi, University of Galway, Ireland 
09:45 - 11:00 Paper Session 1 - Chair: Bharathi Raja Chakravarthi, University of Galway, Ireland 
11:00 - 11:30 Cofee Break 
11:30 - 12:30 Poster Session 1 - Chair: B. Bharathi, SSN College of Engineering, India 
12:30 - 13:30 Lunch Break 
13:30 - 15:45 Paper Session 2 - Chair: Miguel Angel Garcia Cumbreras, University of Jaén, Spain 
15:45 - 16:15 Coffee Break 
16:15 - 17:15 Poster Session 2 - Chair: Gyorgy Kovacs,Lulea University of Technology (LTU), Sweden 
17:15 - 17:45 Plenary and Closing Remarks [**Detailed Program**](https://sites.google.com/view/lt-edi-2024/schedule)

Kavya G

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Presentations

MUCS@DravidianLangTech-2024: Role of Learning Approaches in Strengthening Hate-Alert Systems for Code-mixed Languages

MUCS@DravidianLangTech-2024: A Grid Search Approach to explore Sentiment Analysis in Code-mixed Tamil and Tulu

MUCS@LT-EDI-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification

MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification

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