Malta

Code-mixed languages are increasingly prevalent on social media and online platforms, presenting significant challenges in offensive content detection for natural language processing (NLP) systems. Our study explores how effectively the Sentence Transfer Fine-tuning (Set-Fit) method, combined with logistic regression, detects offensive content in a Tamil-English code-mixed dataset. We compare our model’s performance with five other NLP models: Multilingual BERT (mBERT), LSTM, BERT, IndicBERT, and Language-agnostic BERT Sentence Embeddings (LaBSE). Our model, SetFit, outperforms these models in accuracy, achieving an impressive 89.72%, significantly higher than other models. These results suggest the sentence transformer model’s substantial potential for detecting offensive content in codemixed languages. Our study provides valuable insights into the sentence transformer model’s ability to identify various types of offensive material in Tamil-English online conversations, paving the way for more advanced NLP systems tailored to code-mixed languages.

EACL 2024

SetFit: A Robust Approach for Offensive Content Detection in Tamil-English Code-Mixed Conversations Using Sentence Transfer Fine-tuning

setfit

code-mixed text

offensive language

hate speech detection

### Welcome to the 17th 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 had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. 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.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

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.

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!

Sentiment Analysis (SA) in Dravidian codemixed text is a hot research area right now. In this regard, "Second Shared Task on SA in Code-mixed Tamil and Tulu" at European Chapter of the Association for Computational Linguistics (EACL-2024) is organized. Two tasks namely SA in Tamil-English and Tulu-English code-mixed data make up this shared assignment. In total 64 teams registered for the shared task out of which 19 and 17 systems were received for Tamil and Tulu languages respectively. The performance of the systems submitted by the participants were evaluated based on F1-score.The best method obtained macro average F1-Score of 0.260 and 0.584 for code-mixed Tamil and Tulu texts, respectively.

Overview of Second Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu

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

The Mvskoke language, an indigenous language in the US, has experienced an increase in language revitalization activity in the last few years. This talk is an overview of current language reclamation activities as well as how NLP can come alongside to support community efforts. I will review four NLP tools that are relevant to the Mvskoke case - morphological analysis, TTS, ASR, and corpus search, and discuss challenges and benefits for language revitalization.

Technology and Language Revitalization: A Roadmap for the Mvskoke Language

Blackfoot is challenging for English speaking instructors and learners to acquire because it exhibits unique pitch patterns. This study presents MeTILDA (Melodic Transcription in Language Documentation and Application) as a solution to teaching pitch patterns distinct from English. Specifically, we explore ways to improve data visualization through a visualized pronunciation teaching guide called Pitch Art. The working materials can be downloaded or stored in the cloud for further use and collaboration. These features are aimed to facilitate teachers in developing curriculum for learning pronunciation, and provide students with an interactive and integrative learning environment to better understand Blackfoot language and pronunciation.

Cloud-based Platform for Indigenous Language Sound Education

Descriptive linguistics is a sub-field of linguistics that involves the collection and annotationof language resources to describe linguistic phenomena. The transcription of these resources is often described as a tedious task, and Automatic Speech Recognition (ASR) has frequently been employed to support this process. However, the typical research approach to ASR in documentary linguistics often only captures a subset of the field’s diverse reality. In this paper, we focus specifically on one type of data known as grammaticality judgment elicitation in the context of documenting Kréyòl Gwadloupéyen. We show that only a few minutes of speech is enough to fine-tune a model originally trained in French to transcribe segments in Kréyol.

Automatic Transcription of Grammaticality Judgements for Language Documentation

This abstract outlines how members of various fragmented Celtic language com-
munities have, over the last few years, improved inter-community collaboration.
This collaboration has manifested itself in quite a diverse way. In particular, this
abstract focuses on the improved collaboration between the Welsh and Breton
language technology communities.

How collaboration between Celtic language communities has improved

We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work addresses the gap in linguistic technology for underrepresented languages by collecting and processing data to build a speech synthesis system. We begin our study by gathering a substantial dataset of Mundari text and speech and train end-to-end speech models. We also delve into the methods used for training our models, ensuring they are efficient and effective despite the data constraints. We evaluate our system with native speakers and objective metrics, demonstrating its potential as a tool for preserving and promoting the Mundari language in the digital age.

MunTTS: A Text-to-Speech System for Mundari

In this project, we develop an electronic corpus for Nepalbhāsā, a transhimalayan language indigenous to the Kathmandu Valley and surrounding areas in Nepal. We use web content scraped from four Nepalbhāsā news publications' websites; filters out all content in languages other than Nepalbhāsā; and carry out encoding adjustment of data, such as dates from the indigenous calendar of the speaker community, by scraping the Gregorian dates from the metadata of published news pieces. We discuss further challenges in encoding adjustment, given dates from multiple indigenous calendar systems in Nepalbhāsā newspapers from the early 1920s. We also discuss the project's immediate prospects and its beneficiaries from the speaker community.

Developing a Nepalbhāsā E-corpus and Challenges in Encoding Adjustment

We investigate the performance of state-of-the-art neural ASR systems in transcribing audio recordings for Hupa, a critically endangered language of the Hoopa Valley Tribe. We also explore the impact on ASR performance when augmenting a small dataset of gold-standard high-quality transcriptions with a) a larger dataset with transcriptions of lower quality, and b) model-generated transcriptions in a self- training approach. An evaluation of both dataaugmentation approaches shows that the self- training approach is competitive, producing better WER scores than models trained with no additional data and not lagging far behind models trained with additional lower quality manual transcriptions instead: the deterioration in WER score is just 4.85 points when all the additional data is used in experiments with the best performing system, Wav2Vec. These findings have encouraging implications on the use of ASR systems for transcription and language documentation efforts in the Hupa language

Looking within the self: Investigating the Impact of Data Augmentation with Self-training on Automatic Speech Recognition for Hupa

We explore loss functions for fact verification in the FEVER shared task. While the cross-entropy loss is a standard objective for training verdict predictors, it fails to capture the heterogeneity among the FEVER verdict classes. In this paper, we develop two task-specific objectives tailored to FEVER. Experimental results confirm that the proposed objective functions outperform the standard cross-entropy. Performance is further improved when these objectives are combined with simple class weighting, which effectively overcomes the imbalance in the training data. The source code is available (https://github.com/yuta-mukobara/RLF-KGAT).

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Overview of Second Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu

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