Miracle-dz/UniFAR-FTUs huggingface dataset:
Recognising Occupational Titles in German Parliamentary Debates

The application of text mining methods is becoming more and more popular, not only in Digital Humanities (DH) and Computational Social Sciences (CSS) in general, but also in vocational education and training (VET) research. Employing algorithms offers the possibility to explore corpora that are simply too large for manual methods. However, challenges arise when dealing with abstract concepts like occupations or skills, which are crucial subjects of VET research. Since algorithms require concrete instructions, either in the form of rules or annotated examples, these abstract concepts must be broken down as part of the operationalisation process. In our paper, we tackle the task of identifying occupational titles in the plenary protocols of the German Bundestag. The primary focus lies in the comparative analysis of two distinct approaches: a dictionary-based method and a BERT fine-tuning approach. Both approaches are compared in a quantitative evaluation and applied to a larger corpus sample. Results indicate comparable precision for both approaches (0.93), but the BERT-based models outperform the dictionary-based approach in terms of recall (0.86 vs. 0.77). Errors in the dictionary-based method primarily stem from the ambiguity of occupational titles (e.g., ‘baker’ as both a surname and a profession) and missing terms in the dictionary. In contrast, the BERT model faces challenges in distinguishing occupational titles from other personal names, such as ‘mother’ or ‘Christians’.
Trainable Hard Negative Examples in Contrastive Learning for Unsupervised Abstractive Summarization

Contrastive learning has demonstrated promising results in unsupervised abstractive summarization. However, existing methods rely on manually crafted negative examples, demanding substantial human effort and domain knowledge. Moreover, these human-generated negative examples may be poor in quality and lack adaptability during model training. To address these issues, we propose a novel approach that learns trainable negative examples for contrastive learning in unsupervised abstractive summarization, which eliminates the need for manual negative example design. Our framework introduces an adversarial optimization process between a negative example network and a representation network (including the summarizer and encoders). The negative example network is trained to synthesize hard negative examples that are close to the positive examples, driving the representation network to improve the quality of the generated summaries. We evaluate our method on two benchmark datasets for unsupervised abstractive summarization and observe significant performance improvements compared to strong baseline models.
KEC-AI-NLP@LT-EDI-2024:Homophobia and Transphobia Detection in Social Media Comments using Machine Learning

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. Our team participated in the shared task organized by LTEDI@EACL 2024 and secured seventh rank in the task of Homophobia/Transphobia Detection in social media comments in Tamil with a macro- f1 score of 0.315. Also, our run was submitted for the English language and secured eighth rank with a macro-F1 score of 0.369. The run submitted for Malayalam language securing fourth rank with a macro- F1 score of 0.883 using the Random Forest model.
Transformers@DravidianLangTech-EACL2024: Sentiment Analysis of Code-Mixed Tamil Using RoBERTa

In recent years, there has been a persistent focus on developing systems that can automatically identify the hate speech content circulating on diverse social media platforms. This paper describes the team Transformers’ submission to the Caste/Immigration Hate Speech Detection in Tamil shared task by LT-EDI 2024 workshop at EACL 2024. We used an ensemble approach in the shared task, combining various transformer-based pre-trained models using majority voting. The best macro average F1-score achieved was 0.82. We secured the 1st rank in the Caste/Immigration Hate Speech in Tamil shared task.
Extreme Fine-tuning: A Novel and Fast Fine-tuning Approach for Text Classification

Although fine-tuning a pre-trained model with a conventional approach has shown to be effective in various downstream tasks, previous work has used only backpropagation to fine-tune the model, which causes a massive amount of computational resources and time. We propose Extreme Fine-Tuning (EFT), a novel approach for fine-tuning a pre-trained model effectively and efficiently. EFT uses backpropagation for a brief fine-tuning and an iterative extreme learning machine for training a classifier. We applied EFT to four text classification datasets, MELD, IEMOCAP, IMDb, and AG News, and compared its performance with state-of-the-art (SOTA) approaches. The results indicate that EFT noticeably outperformed the other approaches in training-time measurement with comparable model performance. We will release our code at https://github.com/up-33/extreme-fine-tuning.
SetFit: A Robust Approach for Offensive Content Detection in Tamil-English Code-Mixed Conversations Using Sentence Transfer Fine-tuning

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.
PSILENCE: A Pseudonymization Tool for International Law

Since the announcement of the GDPR, the pseudonymization of legal documents has become a high-priority task in many legal organizations. This means that for making public a document, it is necessary to redact the identity of certain entities, such as witnesses. In this work, we present the first results obtained by PSILENCE, a pseudonymization tool created for redacting semi-automatically international arbitration documents in English. PSILENCE has been built using a Named Entity Recognition (NER) system, along with a Coreference Resolution system. These systems allow us to find the people that we need to redact in a clustered way, but also to propose the same pseudonym throughout one document. This last aspect makes it easier to read and comprehend a redacted legal document. Different experiments were done on four different datasets, one of which was legal, and the results are promising, reaching a Macro F-score of up to 0.72 on the legal dataset.
pyTLEX: A Python Library for TimeLine EXtraction

pyTLEX is an implementation of the TimeLine EXtraction algorithm (TLEX; Finlayson et al.,2021) that enables users to work with TimeML annotations and perform advanced temporal analysis, offering a comprehensive suite of features. TimeML is a standardized markup language for temporal information in text. pyTLEX allows users to parse TimeML annotations, construct TimeML graphs, and execute the TLEX algorithm to effect complete timeline extraction. In contrast to previous implementations (i.e., jTLEX for Java), pyTLEX sets itself apart with a range of advanced features. It introduces a React-based visualization system, enhancing the exploration of temporal data and the comprehension of temporal connections within textual information. Furthermore, pyTLEX incorporates an algorithm for increasing connectivity in temporal graphs, which identifies graph disconnectivity and recommends links based on temporal reasoning, thus enhancing the coherence of the graph representation. Additionally, pyTLEX includes a built-in validation algorithm, ensuring compliance with TimeML annotation guidelines, which is essential for maintaining data quality and reliability. pyTLEX equips researchers and developers with an extensive toolkit for temporal analysis, and its testing across various datasets validates its accuracy and reliability.
Incorporating Word Count Information into Depression Risk Summary Generation: INF@UoS CLPsych 2024 Submission

Large language model classifiers do not directly offer transparency: it is not clear why one class is chosen over another. In this work, summaries explaining the suicide risk level assigned using a fine-tuned mental-roberta-base model are generated from key phrases extracted using SHAP explainability using Mistral-7B. The training data for the classifier consists of all Reddit posts of a user in the University of Maryland Reddit Suicidality Dataset, Version 2, with their suicide risk labels along with selected features extracted from each post by the Linguistic Inquiry and Word Count (LIWC-22) tool. The resulting model is used to make predictions regarding risk on each post of the users in the evaluation set of the CLPsych 2024 shared task, with a SHAP explainer used to identify the phrases contributing to the top scoring, correct and severe risk categories. Some basic stoplisting is applied to the extracted phrases, along with length based filtering, and a locally run version of Mistral-7B-Instruct-v0.1 is used to create summaries from the highest value (based on SHAP) phrases.
A Few-Shot Multi-Accented Speech Classification for Indian Languages using Transformers and LLM’s Fine-Tuning Approaches

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

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.
Explainable Depression Detection Using Large Language Models on Social Media Data

Due to the rapid growth of user interaction on different social media platforms, publicly available social media data has increased substantially. The sheer amount of data and level of personal information being shared on such platforms has made analyzing textual information to predict mental disorders such as depression a reliable preliminary step when it comes to psychometrics. In this study, we first proposed a system to search for texts that are related to depression symptoms from the Beck’s Depression Inventory (BDI) questionnaire, and providing a ranking for further investigation in a second step. Then, in this second step, we address the even more challenging task of automatic depression level detection, using writings and voluntary answers provided by users on Reddit. Several Large Language Models (LLMs) were applied in experiments. Our proposed system based on LLMs can generate both predictions and explanations for each question. By combining two LLMs for different questions, we achieved better performance on three of four metrics compared to the state-of-the-art and remained competitive on the one remaining metric. In addition, our system is explainable on two levels: first, knowing the answers to the BDI questions provides clues about the possible symptoms that could lead to a clinical diagnosis of depression; second, our system can explain the predicted answer for each question.
MUCS@LT-EDI-2024: Exploring Joint Representation for Memes Classification

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)+ResNet50, 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.
Ethical thematic and topic modelling analysis of sleep concerns in a social media derived suicidality dataset

Objective: A thematic and topic modelling analysis of sleep concerns in a social media derived, privacy-preserving, suicidality dataset. This forms the basis for an exploration of sleep as a potential computational linguistic signal in suicide prevention. Background: Suicidal ideation is a limited signal for suicide. Developments in computational linguistics and mental health datasets afford an opportunity to investigate additional signals and to consider the broader clinical ethical design implications. Methodology: A clinician-led integration of reflexive thematic analysis, with machine learning topic modelling (Bertopic), and the purposeful sampling of the University of Maryland Suicidality Dataset. Results: Sleep as a place of refuge and escape, revitalisation for exhaustion, and risk and vulnerability were generated as core themes in an initial thematic analysis of 546 posts. Bertopic analysing 21,876 sleep references in 16791 posts facilitated the production of 40 topics that were clinically interpretable, relevant, and thematically aligned to a level that exceeded original expectations. Privacy and synthetic representative data, reproducibility, validity and stochastic variability of results, and a multi-signal formulation perspective, are highlighted as key research and clinical issues.
Quartet@LT-EDI 2024: A SVM-ResNet50 Approach For Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes

Meme is a very popular term prevailing among almost all social media platforms in recent days. A meme can be a combination of text and image whose sole purpose is meant to be funny and entertain people. Memes can sometimes promote misogynistic content expressing hatred, contempt, or prejudice against women. The Shared Task LT–EDI 2024: Multitask Meme Classification: Unraveling Misogynistic and Trolls in Online Memes Task 1 was created with the purpose to classify social media memes as “misogynistic” and “Non - Misogynistic”. The task encompassed Tamil and Malayalam datasets. We separately classified the textual data using Multinomial Naive Bayes and pictorial data using ResNet50 model. The results of from both data were combined to yield an overall result. We were ranked 2nd for both languages in this task.
Did the Names I Used within My Essay Affect My Score? Diagnosing Name Biases in Automated Essay Scoring

Automated essay scoring (AES) 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 to other reasons, such as personal names used in the text of the essay. Moreover, most of the research data for AES tends to contain personal identifiable information. Because of that, pseudonymization becomes an important tool to make sure that this data can be freely shared. Thus, our systems should not grade students based on which given names were used in the text of the essay, both for fairness and for privacy reasons. In this paper we explore how given names affect the CEFR level classification of essays of second language learners of Swedish. We use essays containing just one personal name and substitute it for names from lists of given names from four different ethnic origins, namely Swedish, Finnish, Anglo-American, and Arabic. We find that changing the names within the essays has no apparent effect on the classification task, regardless of whether a feature-based or a transformer-based model is used.
T is for Treu, but how do you pronounce that? Using C-LARA to create phonetic texts for Kanak languages

In Drehu, a language of the indigenous Kanak people of New Caledonia, the word treu ‘moon’ is pronounced [{tSe.u}]; but, even if they hear the word, the spelling pulls French speakers to a spurious pronunciation [tK{o}]. We implement a strategy to mitigate the influence of such orthographic conflicts, while retaining the benefits of written input on vocabulary learning. We present text in “phonetized” form, where words are broken down into components associated with mnemonically presented phonetic values, adapting features from the “Comment ça se prononce~? ” multilingual phonetizer. We present an exploratory project where we used the ChatGPT-based Learning And Reading Assistant (C-LARA) to implement a version of the phonetizer strategy, outlining how the AI-engineered codebase and help from the AI made it easy to add the necessary extensions. We describe two proof-of-concept texts for learners produced using the platform, a Drehu alphabet book and a Drehu version of “The (North) Wind and the Sun”; both texts include native-speaker recorded audio, pronunciation respellings based on French orthography, and AI-generated illustrations.
Enhancing Society-Undermining Disinformation Detection through Fine-Grained Sentiment Analysis Pre-Finetuning

In the era of the digital world, while freedom of speech has been flourishing, it has also paved the way for disinformation, causing detrimental effects on society. Legal and ethical criteria are insufficient to address this concern, thus necessitating technological intervention. This paper presents a novel method leveraging pre-finetuning concept for efficient detection and removal of disinformation that may undermine society, as deemed by judicial entities. We argue the importance of detecting this type of disinformation and validate our approach with real-world data derived from court orders. Following a study that highlighted four areas of interest for rumor analysis, our research proposes the integration of a fine-grained sentiment analysis task in the pre-finetuning phase of language models, using the GoEmotions dataset. Our experiments validate the effectiveness of our approach in enhancing performance significantly. Furthermore, we explore the application of our approach across different languages using multilingual language models, showing promising results. To our knowledge, this is the first study that investigates the role of sentiment analysis pre-finetuning in disinformation detection.
Quartet@LT-EDI 2024: Support Vector Machine Based Approach For Homophobia/Transphobia Detection In Social Media Comments

Homophobia and transphobia are terms which are used to describe the fear or hatred towards people who are attracted to the same sex or people whose psychological gender differs from his biological sex. People use social media to exert this behaviour. The increased amount of abusive content negatively affects people in a lot of ways. It makes the environment toxic and unpleasant to LGBTQ+ people. The paper talks about the classification model for classifying the contents into 3 categories which are homophobic, transphobic and nonhomophobic/ transphobic. We used many traditional models like Support Vector Machine, Random Classifier, Logistic Regression and KNearest Neighbour to achieve this. The macro average F1 scores for Malayalam, Telugu, English, Marathi, Kannada, Tamil, Gujarati, Hindi are 0.88, 0.94, 0.96, 0.78, 0.93, 0.77, 0.94, 0.47 and the rank for these languages are 5, 6, 9, 6, 8, 6, 6, 4.