Malta

Linguistic data can — and often does — contain PII (Personal Identifiable Information). Both from a legal and ethical standpoint, the sharing of such data is not permissible. According to the GDPR, pseudonymization, i.e. the replacement of sensitive information with surrogates, is an acceptable strategy for privacy preservation. While research has been conducted on the detection and replacement of sensitive data in Swedish medical data using Large Language Models (LLMs), it is unclear whether these models handle PII in less structured and more thematically varied texts equally well. In this paper, we present and discuss the performance of an LLM-based PII-detection system for Swedish learner essays.

EACL 2024

Detecting Personal Identifiable Information in Swedish Learner Essays

l2 corpora

sensitive data detection

pii detection

pseudonymization

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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

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.

Large language models in public-facing industrial applications must accurately process data for the domain in which they are deployed, but they must not leak sensitive or confidential information when used. We present a process for anonymizing training data, a framework for quantitatively and qualitatively assessing the effectiveness of this process, and an assessment of the effectiveness of models fine-tuned on anonymized data in comparison with commercially available LLM APIs.

Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts

Clinical data, in the form of electronic health records, are rich resources that can be tapped using natural language processing. At the same time, they contain very sensitive information that must be protected. One strategy is to remove or obscure data using automatic de-identification. However, the detection of sensitive data can yield false positives. This is especially true for tokens that are similar in form to sensitive entities, such as eponyms. These names tend to refer to medical procedures or diagnoses rather than specific persons. Previous research has shown that automatic de-identification systems often misclassify eponyms as names, leading to a loss of valuable medical information. In this study, we estimate the prevalence of eponyms in a real Swedish clinical corpus. Furthermore, we demonstrate that modern transformer-based de-identification systems are more accurate in distinguishing between names and eponyms than previous approaches.

When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification

Automated essay scoring of second-language learner essays is a high-stakes task, as it can affect the job and educational opportunities a student may have access to. Thus, it becomes imperative to make sure that the essays are graded based on the students' language proficiency as opposed other reasons, such as personal names used in the text of the essay. Moreover, most of the research data is not freely accessible, as it tends to contain personal identifiable information. Because of that, pseudonlymization becomes an important tool to make sure that this data can be freely shared. Thus, our systems should not grade students based on which first names were used in the text of the essay, both for fairness and for privacy reasons. In this paper we explore how first names affect the CEFR level classification of essays of second language learners of Swedish. We used essays containing just one personal name and substituted it for names from lists of first names from four different ethnic origins, namely Swedish, Finnish, Anglo-American, and Arabic. We found that changing the names within the essays had no apparent effect on the classification task, regardless of whether a feature-based or a transformer-based model was used.

Did the Names I Used within My Essay Affect My Score? Diagnosing Name Biases in Automated Essay Scoring

We describe the second-place submission for the shared task organized at the Fourth Workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2024). The task focuses on detecting caste/immigration hate speech in Tamil. The included texts involve the Tamil language in both Tamil script and transliterated into Latin script, with some texts also in English. Considering different scripts, we examined the performance of 12 transformer language models on the dev set. Our analysis revealed that for the whole dataset, the model google/muril-large-cased performs the best. We used an ensemble of several models for the final challenge submission, achieving 0.81 for the test dataset.

kubapok@LT-EDI 2024: Evaluating Transformer Models for Hate Speech Detection in Tamil

This paper describes our homophobia/transphobia in social media comments detection system developed as part of the shared task at LT-EDI-2024. We took a transformer-based approach to develop our multiclass classification model for ten language conditions (English, Spanish, Gujarati, Hindi, Kannada, Malayalam, Marathi, Tamil, Tulu, and Telugu). We introduced synthetic and organic instances of script-switched language data during domain adaptation to mirror the linguistic realities of social media language as seen in the labelled training data. Our system ranked second for Gujarati and Telugu with varying levels of performance for other language conditions. The results suggest incorporating elements of paralinguistic behaviour such as script-switching may improve the performance of language detection systems especially in the cases of under-resourced languages conditions.

cantnlp@LT-EDI-2024: Automatic Detection of Anti-LGBTQ+ Hate Speech in Under-resourced Languages

This research focuses on Homophobia and Transphobia Detection in South Indian languages, specifically Telugu, Kannada, Tamil, and Malayalam. Leveraging the Homophobia/Transphobia Detection dataset, we propose an innovative approach employing a custom-designed tokenizer with a Bidirectional Long Short-Term Memory (BiLSTM) architecture. Our distinctive contribution lies in a tokenizer that reduces model sizes to below 7MB, improving efficiency and addressing real-time deployment challenges. The BiLSTM implementation demonstrates significant enhancements in hate speech detection accuracy, effectively capturing linguistic nuances. Low-size models efficiently alleviate inference challenges, ensuring swift real-time detection and practical deployment. This work pioneers a framework for hate speech detection, providing insights into model size, inference speed, and real-time deployment challenges in combatting online hate speech within South Indian languages.

byteLLM@LT-EDI-2024: Homophobia/Transphobia Detection in Social Media Comments - Custom Subword Tokenization with Subword2Vec and BiLSTM

In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.

MasonTigers@LT-EDI-2024: An Ensemble Approach towards Detecting Homophobia and Transphobia in Social Media Comments

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

Our work addresses the growing concern of abusive comments in online platforms, particularly focusing on the identification of Homophobia and Transphobia in social media comments. The goal is to categorize comments into three classes: Homophobia, Transphobia, and non-anti LGBT+ comments. Utilizing machine learning techniques and a deep learning model, our work involves training on a English dataset with a designated training set and testing on a validation set. This approach aims to contribute to the understanding and detection of Homophobia and Transphobia within the realm of social media interactions.In the era of digital communication, social media platforms play a crucial role in facilitating interactions, education, and information exchange. YouTube, a prominent social networking and video-sharing platform, allows users to create accounts, share videos, and engage with their audience through comments. Unfortunately, the presence of abusive comments on videos or responses to other users' comments can be offensive and detrimental to the mental well-being of platform users, especially when it comes to transgender and homo-sexual people.The surge in online communication has brought forth concerns regarding the dissemination of slander, hate speech, and online bullying. Slander, defined as a false spoken statement damaging to a person or group, is increasingly recognized for its harmful impact on individuals and society. Negative comments not only inflict psychological harm but also contribute to the spread of humor, division, and discontent online. Recognizing the profound consequences of hate speech, major platforms like YouTube, Facebook, Instagram, and Twitter have instituted policies and protocols to address hateful content and counteract negative behaviors. The objective of our study is to analyze and pinpoint offensive comments within an English dataset. English, known for its analytical or isolating nature, conveys grammatical relationships through word order and auxiliary words rather than extensive affixation. The task of detecting abusive comments is treated as a text classification problem, involving the extraction of features from the text and assigning it to predefined categories. This is achieved through a linear classifier operating on sentence embeddings. We conducted training on a variety of machine learning and deep learning models to identify abusive comments in English, and the results were compared to determine the optimal model.

KEC-AI-NLP@LT-EDI-2024:Homophobia and Transphobia Detection in social media comments using Machine Learning

This study investigates the robustness and generalization of transformer-based models for automatic media bias detection. We explore the behavior of current bias classifiers by analyzing feature attributions and stress-testing with adversarial datasets. The findings reveal a disproportionate focus on rare but strongly connotated words, suggesting a rather superficial understanding of linguistic bias and challenges in contextual interpretation. This problem is further highlighted by inconsistent bias assessment when stress-tested with different entities and minorities. Enhancing automatic media bias detection models is critical to improving inclusivity in media, ensuring balanced and fair representation of diverse perspectives.

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Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts

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SPY: Enhancing Privacy with Synthetic PII Detection Dataset

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