Accented speech classification plays a vital role in the advancement of high-quality automatic speech recognition (ASR) technology. For certain applications, like multi-accented speech classification, it is not always viable to obtain data with accent variation, especially for resource-poor languages. This is one of the major reasons that contributes to the underperformance of the speech classification systems. Therefore, in order to handle speech variability in Indian language speaker accents, we propose a few-shot learning paradigm in this study. It learns generic feature embeddings using an encoder from a pre-trained whisper model and a classification head for classification. The model is refined using LLM's fine-tuning techniques, such as LoRA and QLoRA, for the six Indian English accents in the Indic Accent Dataset. The experimental findings show that the accuracy of the model is greatly increased by the few-shot learning paradigm's effectiveness combined with LLM's fine-tuning techniques. In optimal settings, the model's accuracy can reach 94% when the trainable parameters are set to 5%.

A Few-Shot Multi-Accented Speech Classification for Indian Languages using Transformers and LLM’s Fine-Tuning Approaches

Identifying fake news hidden as real news is crucial to fight misinformation and ensure reliable information, especially in resource-scarce languages like Malayalam. To recognize the unique challenges of fake news in languages like Malayalam, we present a dataset curated specifically for the classification of fake news in Malayalam. This fake news is categorized based on the degree of misinformation, marking the first of its kind in this language. Further, we propose baseline models employing multilingual BERT and diverse machine learning classifiers. Our findings indicate that logistic regression trained on LaBSE features demonstrates promising initial performance with an F1 score of 0.3393. However, addressing the significant data imbalance remains essential for further improvement in model accuracy.

From Dataset to Detection: A Comprehensive Approach to Combating Malayalam Fake News

The Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech-2024) at EACL 2024 featured a shared task on Hate and Offensive Language Detection in Telugu Codemixed Text (HOLD-Telugu). This paper examines the submissions of various participating teams to the task. In order to identify the contents containing harmful information in Telugu codemixed social media text, the challenge pushes researchers and academicians to build models. The dataset for the task was created by gathering YouTube comments. Using the macro F1-score, the supplied models were assessed, and the rank list was created as a result.

Findings of the Shared Task on Hate and Offensive Language Detection in Telugu Codemixed Text (HOLD-Telugu)@DravidianLangTech 2024

As part of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech-2024) at EACL, this paper presents the shared challenge of multimodal sentiment analysis, abusive language detection and hate speech detection in Dravidian languages. Through this shared work, researchers worldwide can submit their models for three crucial social media data analysis challenges in Dravidian languages - sentiment analysis, abusive language detection, and hate speech detection. This aim is to build models for deriving fine-grained sentiment analysis from multimodal data in Tamil and Malayalam, identifying abusive and hate content from multimodal data in Tamil. Three modalities make up the multimodal data: text, audio, and video. YouTube videos were gathered to create the datasets for the tasks. Thirty-none teams took part in the competition. However, only two teams, though, turned in their findings. The macro F1-score was used to assess the submissions.

Findings of the Shared Task on Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL)@DravidianLangTech 2024

The rise of online social media has revolutionized communication, offering users a convenient way to share information and stay updated on current events. However, this surge in connectivity has also led to the proliferation of misinformation, commonly known as fake news. This misleading content, often disguised as legitimate news, poses a significant challenge as it can distort public perception and erode trust in reliable sources. This shared task consists of two subtasks such as task 1 and task 2. Task 1 aims to classify a given social media text into original or fake. The goal of the FakeDetect-Malayalam task2 is to encourage participants to develop effective models capable of accurately detecting and classifying fake news articles in the Malayalam language into different categories like False, Half True, Mostly False, Partly False, and Mostly True. For this shared task, 33 participants submitted their results.

Overview of the Second Shared Task on Fake News Detection in Dravidian Languages: DravidianLangTech@EACL 2024

Speech recognition is known to be a specialized application of speech processing. Automatic speech recognition (ASR) systems are designed to perform the speech-to-text task. Although ASR systems have been the subject of extensive research, they still encounter certain challenges when speech variations arise. The speaker’s age, gender, vulnerability, and other factors are the main causes of the variations in speech. In this work, we propose a fine-tuned speech recognition model for recognising the spoken words of vulnerable individuals in Tamil. This research utilizes a dataset sourced from the LT-EDI@EACL2024 shared task. We trained and tested pre-trained ASR models, including XLS-R and Whisper. The findings highlight that the fine-tuned Whisper ASR model surpasses the XLS-R, achieving a word error rate (WER) of 24.452, signifying its superior performance in recognizing speech from diverse individuals.

CEN_Amrita@LT-EDI 2024: A Transformer based Speech Recognition System for Vulnerable Individuals in Tamil

This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.

Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages

[**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)

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!

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)

W3: Fourth Workshop on Language Technology for Equality, Diversity and Inclusion (LTEDI)

**Organizers:** Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Parameswari
Krishnamurthy, Elizabeth Sherly, Sinnathamby Mahesan
 **Description:** 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.
 
**Please visit our [website](https://dravidianlangtech.github.io/2022/)**

(Dravidian LangTech) The Second Workshop on Speech and Language Technologies for DravidianLanguages

findings / work in progress

# 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) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

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). 

Premjith B

7

9

Presentations

A Few-Shot Multi-Accented Speech Classification for Indian Languages using Transformers and LLM’s Fine-Tuning Approaches

From Dataset to Detection: A Comprehensive Approach to Combating Malayalam Fake News

Findings of the Shared Task on Hate and Offensive Language Detection in Telugu Codemixed Text (HOLD-Telugu)@DravidianLangTech 2024

Findings of the Shared Task on Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL)@DravidianLangTech 2024

Overview of the Second Shared Task on Fake News Detection in Dravidian Languages: DravidianLangTech@EACL 2024

CEN_Amrita@LT-EDI 2024: A Transformer based Speech Recognition System for Vulnerable Individuals in Tamil

Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages

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