Malta

We propose a method that integrates supervised extractive and generative language models for providing supporting evidence of suicide risk in the CLPsych 2024 shared task. Our approach comprises three steps. Initially, we construct a BERT-based model for estimating sentence-level suicide risk and negative sentiment. Next, we precisely identify high suicide risk sentences by emphasizing elevated probabilities of both suicide risk and negative sentiment. Finally, we integrate generative summaries using the MentaLLaMa framework and extractive summaries from identified high suicide risk sentences and a specialized dictionary of suicidal risk words. SophiaADS, our team, achieved 1st place for highlight extraction and ranked 10th for summary generation, both based on recall and consistency metrics, respectively.

EACL 2024

Integrating Supervised Extractive and Generative Language Models for Suicide Risk Evidence Summarization

suicide risk classification

mentallama

bert

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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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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.

Hate speech refers to the offensive remarks against a community or individual based on inherent characteristics. Hate speech against a community based on their caste and native are unfortunately prevalent in the society. Especially with social media platforms being a very popular tool for communication and sharing ideas, people post hate speech against caste or immigrants on social medias. The Shared Task LT–EDI 2024: Caste/Immigration Hate Speech Detection\cite{chakravarthi-caste-2024-overview} was created with the objective to create an automatic classification system that detects and classifies hate speech posted on social media targeting a community belonging to a particular caste and immigrants. Datasets in Tamil language were provided along with the shared task. We experimented with several traditional models such as Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier and Decision Tree Classifier out of which Support Vector Machine yielded the best results placing us 8th in the rank list released by the organizers.

Quartet@LT-EDI 2024: A Support Vector Machine Approach For Caste/Immigration Hate Speech Detection

This paper presents our submission for Shared task on Stress Identification in Dravidian Languages: StressIdent LT-EDI@EACL2024. The objective of this task is to identify stress levels in individuals based on their social media content. The system is tasked with analysing posts written in a code-mixed language of Tamil and Telugu and categorising them into two labels: "stressed" or "not stressed." Our approach aimed to leverage feature extraction and juxtapose the performance of widely used traditional, deep learning and transformer models. Our research highlighted that building a pipeline with traditional classifiers proved to significantly improve their performance (0.98 and 0.93 F1-scores in Telugu and Tamil respectively), surpassing the baseline as well as deep learning and transformer models.

SSN-Nova@LT-EDI 2024: Leveraging Vectorisation Techniques in an Ensemble Approach for Stress Identification in Low-Resource Languages

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.

Quartet@LT-EDI 2024: A SVM-ResNet50 Approach For Multitask Meme classification - Unraveling Misogynistic and Trolls in Online Memes

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 non-homophobic/transphobic. We used many traditional models like Support Vector Machine, Random Classifier, Logistic Regression and K- Nearest 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.

Quartet@LT-EDI 2024: Support Vector Machine based approach for Homophobia/Transphobia Detection in social media comments

This paper presents our submission for the shared task on Caste/Immigration Hate Speech Detection: LT-EDI@EACL 2024. This text classification task aims to foster the creation of models capable of identifying hate speech related to caste and immigration. The dataset comprises of social media comments, and the goal is to categorise them into negative and positive sentiments. Our approach explores back-translation for data augmentation to address sparse datasets in low-resource Dravidian languages. While Part-of-Speech (POS) tagging is valuable in natural language processing, our work highlights its ineffectiveness in Dravidian languages, with model performance drastically reducing from 0.73 to 0.67 on application. In analysing boosting and ensemble methods, the voting classifier with traditional models outperforms others and the boosting techniques, underscoring the efficacy of simpler models on low-resource data despite augmentation.

SSN-Nova@LT-EDI 2024: POS Tagging, Boosting Techniques and Voting Classifiers for Caste/Immigration Hate Speech Detection

The widespread use of online communication has caused a significant increase in the spread of hate speech on social media. However, there are also hate crimes based on caste or immigration status. Despite India's efforts to bring equality among its citizens, numerous crimes occur just based on caste. Immigration-based hostility happens both in India and in developed countries. A shared task was arranged to address this issue in a low-resource language such as Tamil. This paper aims to improve the detection of hate speech and hostility based on caste and immigration status on social media. To achieve this, we investigated several Machine Learning (ML), Deep Learning (DL), and transformer-based models, including M-BERT, XLM-R, and Tamil BERT. We obtained a macro -score of 0.80 using the M-BERT model, which enabled us to rank on the shared task.

CUET_NLP_Manning_LT-EDI@EACL2024: Transformer Based Approach on Caste or Immigration Hate Speech Detection

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.

Transformers@LT-EDI-EACL2024: Caste/Immigration Hate Speech Detection in Tamil Using Ensembling on Transformers

In this paper, we are discussing the submission we have made for the shared task on “Speech recognition for vulnerable individuals in Tamil” in LT-EDI in the year 2024. In this , we have tried to develop an automatic speech recognition system in the Tamil language. The dataset used was the audio collected from vulnerable individuals such as elderly men and women and transgenders in Tamil. The automatic speech recognition system is designed with pre- trained model Rajaram1996/wav2vec2-large-xlsr-53-tamil. The pretrained model is finetuned with common tamil voice dataset. Now the test dataset is fed into this system and the transcriptions obtained are submitted. The evaluation metric is Word Error Rate(WER). Our system has a WER of 37.733.

DRAVIDIAN LANGUAGE@ LT-EDI 2024:Pretrained Transformer based Automatic Speech Recognition system for Elderly People

Caste and Immigration speech refers to the use of language that distinguishes the offense, violence, and distresses on their social, caste, and immigration status. Here, caste hate speech targets the imbalance of an individual’s social status and focuses mainly on the degradation of their caste group. While the immigration hate speech imposes the differences in nationality, culture, and individual status. These speeches are meant to affront the social status of these people. To detect this hate in the speech, our task on Caste/Immigration Hate Speech Detection has been created which classifies human speech into genuine or stimulate categories. For this task, we used multiple classification models such as the train test split model to split the dataset into train and test data, Logistic regression, Support Vector Machine, MLP (Multi-layer Perceptron) classifier, Random Forest classifier, KNN classifier, and Decision tree classification. Among these models, The SVM gave the highest macro average F1 score of 0.77 and the average accuracy for these models is around 0.75

Algorithm Alliance LT-EDI-2024: Caste/Immigration Hate Speech Detection

Research on psychological risk factors for suicide has developed for decades. However, combining explainable theory with modern data-driven language model approaches is non-trivial. In this study, we propose and evaluate methods for identifying language patterns aligned with theories of suicide risk by combining theory-driven suicidal archetypes with language model-based and relative entropy-based approaches. Archetypes are based on prototypical statements that evince risk of suicidality while relative entropy considers the ratio of how unusual both a risk-familiar and unfamiliar model find the statements. While both approaches independently performed similarly, we find that combining the two significantly improved the performance in the shared task evaluations, yielding our combined system submission with a BERTScore Recall of 0.906. Consistent with the literature, we find that titles are highly informative as suicide risk evidence, despite the brevity. We conclude that a combination of theory- and data-driven methods are needed in the mental health space and can outperform more modern prompt-based methods.

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Next from EACL 2024

Quartet@LT-EDI 2024: A Support Vector Machine Approach For Caste/Immigration Hate Speech Detection

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Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language