ymoslem/acl-abstracts huggingface dataset:
KaustubhSharedTask@LT-EDI 2023: Homophobia-Transphobia Detection in Social Media Comments with NLPAUG-driven Data Augmentation (2023)

Our research in Natural Language Processing (NLP) aims to detect hate speech comments specifically targeted at the LGBTQ+ community within the YouTube platform shared task conducted by LTEDI workshop. The dataset provided by the organizers exhibited a high degree of class imbalance, and to mitigate this, we employed NLPAUG, a data augmentation library. We employed several classification methods and reported the results using recall, precision, and F1-score metrics. The classification models discussed in this paper include a Bidirectional Long Short-Term Memory (BiLSTM) model trained with Word2Vec embeddings, a BiLSTM model trained with Twitter GloVe embeddings, transformer models such as BERT, DistiBERT, RoBERTa, and XLM-RoBERTa, all of which were trained and fine-tuned. We achieved a weighted F1-score of 0.699 on the test data and secured fifth place in task B with 7 classes for the English language.
JudithJeyafreeda@LT-EDI-2023: Using GPT model for recognition of Homophobia/Transphobia detection from social media (2023)

Homophobia and Transphobia is defined as hatred or discomfort towards Gay, Lesbian, Transgender or Bisexual people. With the increase in social media, communication has become free and easy. This also means that people can also express hatred and discomfort towards others. Studies have shown that these can cause mental health issues. Thus detection and masking/removal of these comments from the social media platforms can help with understanding and improving the mental health of LGBTQ+ people. In this paper, GPT2 is used to detect homophobic and/or transphobic comments in social media comments. The comments used in this paper are from five (English, Spanish, Tamil, Malayalam and Hindi) languages. The results show that detecting comments in English language is easier when compared to the other languages.
iicteam@LT-EDI-2023: Leveraging pre-trained Transformers for Fine-Grained Depression Level Detection in Social Media (2023)

Depression is a prevalent mental illness characterized by feelings of sadness and a lack of interest in daily activities. Early detection of depression is crucial to prevent severe consequences, making it essential to observe and treat the condition at its onset. At ACL-2022, the DepSign-LT-EDI project aimed to identify signs of depression in individuals based on their social media posts, where people often share their emotions and feelings. Using social media postings in English, the system categorized depression signs into three labels: {\textquotedblleft}not depressed,{\textquotedblright} {\textquotedblleft}moderately depressed,{\textquotedblright} and {\textquotedblleft}severely depressed.{\textquotedblright} To achieve this, our team has applied MentalRoBERTa, a model trained on big data of mental health. The test results indicated a macro F1-score of 0.439, ranking the fourth in the shared task.
JA-NLP@LT-EDI-2023: Empowering Mental Health Assessment: A RoBERTa-Based Approach for Depression Detection (2023)

Depression, a widespread mental health disorder, affects a significant portion of the global population. Timely identification and intervention play a crucial role in ensuring effective treatment and support. Therefore, this research paper proposes a fine-tuned RoBERTa-based model for identifying depression in social media posts. In addition to the proposed model, Sentence-BERT is employed to encode social media posts into vector representations. These encoded vectors are then utilized in eight different popular classical machine learning models. The proposed fine-tuned RoBERTa model achieved a best macro F1-score of 0.55 for the development dataset and a comparable score of 0.41 for the testing dataset. Additionally, combining Sentence-BERT with Naive Bayes (S-BERT + NB) outperformed the fine-tuned RoBERTa model, achieving a slightly higher macro F1-score of 0.42. This demonstrates the effectiveness of the approach in detecting depression from social media posts.
Team-KEC@LT-EDI: Detecting Signs of Depression from Social Media Text (2023)

The rise of social media has led to a drastic surge in the dissemination of hostile and toxic content, fostering an alarming proliferation of hate speech, inflammatory remarks, and abusive language. The exponential growth of social media has facilitated the widespread circulation of hostile and toxic content, giving rise to an unprecedented influx of hate speech, incendiary language, and abusive rhetoric. The study utilized different techniques to represent the text data in a numerical format. Word embedding techniques aim to capture the semantic and syntactic information of the text data, which is essential in text classification tasks. The study utilized various techniques such as CNN, BERT, and N-gram to classify social media posts into depression and non-depression categories. Text classification tasks often rely on deep learning techniques such as Convolutional Neural Networks (CNN), while the BERT model, which is pre-trained, has shown exceptional performance in a range of natural language processing tasks. To assess the effectiveness of the suggested approaches, the research employed multiple metrics, including accuracy, precision, recall, and F1-score. The outcomes of the investigation indicate that the suggested techniques can identify symptoms of depression with an average accuracy rate of 56{\%}.
cantnlp@LT-EDI-2023: Homophobia/Transphobia Detection in Social Media Comments using Spatio-Temporally Retrained Language Models (2023)

This paper describes our multiclass classification system developed as part of the LT-EDI@RANLP-2023 shared task. We used a BERT-based language model to detect homophobic and transphobic content in social media comments across five language conditions: English, Spanish, Hindi, Malayalam, and Tamil. We retrained a transformer-based cross-language pretrained language model, XLM-RoBERTa, with spatially and temporally relevant social media language data. We found the inclusion of this spatio-temporal data improved the classification performance for all language and task conditions when compared with the baseline. We also retrained a subset of models with simulated script-mixed social media language data with varied performance. The results from the current study suggests that transformer-based language classification systems are sensitive to register-specific and language-specific retraining.
NLP\_CHRISTINE@LT-EDI-2023: RoBERTa \& DeBERTa Fine-tuning for Detecting Signs of Depression from Social Media Text (2023)

The paper describes the system for the 4th Shared task on {\textquotedblleft}Detecting Signs of Depression from Social Media Text{\textquotedblright} at LT-EDI@RANLP 2023, which aimed to identify signs of depression on English social media texts. The solution comprised data cleaning and pre-processing, the use of additional data, a method to deal with data imbalance as well as fine-tuning of two transformer-based pre-trained language models, RoBERTa-Large and DeBERTa-V3-Large. Four model architectures were developed by leveraging different word embedding pooling methods, namely a RoBERTa-Large bidirectional GRU model using GRU pooling and three DeBERTa models using CLS pooling, mean pooling and max pooling, respectively. Although ensemble learning of DeBERTa`s pooling methods through majority voting was employed for better performance, the RoBERTa bidirectional GRU model managed to receive the 8th place out of 31 submissions with 0.42 Macro-F1 score.
IIITDWD@LT-EDI-2023 Unveiling Depression: Using pre-trained language models for Harnessing Domain-Specific Features and Context Information (2023)

Depression has become a common health problem impacting millions of individuals globally. Workplace stress and an unhealthy lifestyle have increased in recent years, leading to an increase in the number of people experiencing depressive symptoms. The spread of the epidemic has further exacerbated the problem. Early detection and precise prediction of depression are critical for early intervention and support for individuals at risk. However, due to the social stigma associated with the illness, many people are afraid to consult healthcare specialists, making early detection practically impossible. As a result, alternative strategies for depression prediction are being investigated, one of which is analyzing users' social media posting behaviour. The organizers of LT-EDI@RANLP carried out a shared Task to encourage research in this area. Our team participated in the shared task and secured 21st rank with a macro F1 score 0f 0.36. This article provides a summary of the model presented in the shared task.
CIMAT-NLP@LT-EDI-2023: Finegrain Depression Detection by Multiple Binary Problems Approach (2023)

This work described the work of the team CIMAT-NLP on the Shared task of Detecting Signs of Depression from Social Media Text at LT-EDI@RANLP 2023, which consists of depression classification on three levels: {\textquotedblleft}not depression{\textquotedblright}, {\textquotedblleft}moderate{\textquotedblright} depression and {\textquotedblleft}severe{\textquotedblright} depression on text from social media. In this work, we proposed two approaches: (1) a transformer model which can handle big text without truncation of its length, and (2) an ensemble of six binary Bag of Words. Our team placed fourth in the competition and found that models trained with our approaches could place second
SIS@LT-EDI-2023: Detecting Signs of Depression from Social Media Text (2023)

Various biological, genetic, psychological or social factors that feature a target oriented life with chronic stress and frequent traumatic experiences, lead to pessimism and apathy. The massive scale of depression should be dealt with as a disease rather than a {\textquoteleft}phase' that is neglected by the majority. However, not a lot of people are aware of depression and its impact. Depression is a serious issue that should be treated in the right way. Many people dealing with depression do not realize that they have it due to the lack of awareness. This paper aims to address this issue with a tool built on the blocks of machine learning. This model analyzes the public social media texts and detects the signs of depression under three labels namely {\textquotedblleft}not depressed{\textquotedblright}, {\textquotedblleft}moderately depressed{\textquotedblright}, and {\textquotedblleft}severely depressed{\textquotedblright} with high accuracy. The ensembled model uses three learners namely Multi-Layered Perceptron, Support Vector Machine and Multinomial Naive Bayes Classifier. The distinctive feature in this model is that it uses Artificial Neural Networks, Classifiers, Regression and Voting Classifiers to compute the final result or output.
TEAM BIAS BUSTERS@LT-EDI-2023: Detecting Signs of Depression with Generative Pretrained Transformers (2023)

This paper describes our methodology adopted to participate in the multi-class classification task under the auspices of the Third Workshop on Language Technology for Equality, Diversity, Inclusion (LT-EDI) in the Recent Advances in Natural Language Processing (RANLP) 2023 conference. The overall objective was to employ ML algorithms to detect signs of depression in English social media content, classifying each post into one of three categories: no depression, moderate depression, and severe depression. To accomplish this we utilized generative pretrained transformers (GPTs), leveraging the full-scale OpenAI API. Our strategy incorporated prompt engineering for zero-shot and few-shot learning scenarios with ChatGPT and fine-tuning a GPT-3 model. The latter approach yielded the best results which allowed us to outperform our benchmark XGBoost classifier based on character-level features on the dev set and score a macro F1 score of 0.419 on the final blind test set.
RANGANAYAKI@LT-EDI: Hope Speech Detection using Capsule Networks (2023)

HOPE speeches convey uplifting and motivating messages that help enhance mental health and general well-being. Hope speech detection has gained popularity in the field of natural language processing as it gives people the motivation they need to face challenges in life. The momentum behind this technology has been fueled by the demand for encouraging reinforcement online. In this paper, a deep learning approach is proposed in which four different word embedding techniques are used in combination with capsule networks, and a comparative analysis is performed to obtain results. Oversampling is used to address class imbalance problem. The dataset used in this paper is a part of the LT-EDI RANLP 2023 Hope Speech Detection shared task. The approach proposed in this paper achieved a Macro Average F1 score of 0.49 and 0.62 in English and Hindi-English code mix test data, which secured 2nd and 3rd rank respectively in the above mentioned share task.
TechSSN1 at LT-EDI-2023: Depression Detection and Classification using BERT Model for Social Media Texts (2023)

Depression is a severe mental health disorder characterized by persistent feelings of sadness and anxiety, a decline in cognitive functioning resulting in drastic changes in a human`s psychological and physical well-being. However, depression is curable completely when treated at a suitable time and treatment resulting in the rejuvenation of an individual. The objective of this paper is to devise a technique for detecting signs of depression from English social media comments as well as classifying them based on their intensity into severe, moderate, and not depressed categories. The paper illustrates three approaches that are developed when working toward the problem. Of these approaches, the BERT model proved to be the most suitable model with an F1 macro score of 0.407, which gave us the 11th rank overall.
SANBAR@LT-EDI-2023:Automatic Speech Recognition: vulnerable old-aged and transgender people in Tamil (2023)

An Automatic Speech Recognition systems for Tamil are designed to convert spoken lan- guage or speech signals into written Tamil text. Seniors go to banks, clinics and authoritative workplaces to address their regular necessities. A lot of older people are not aware of the use of the facilities available in public places or office. They need a person to help them. Like- wise, transgender people are deprived of pri- mary education because of social stigma, so speaking is the only way to help them meet their needs. In order to build speech enabled systems, spontaneous speech data is collected from seniors and transgender people who are deprived of using these facilities for their own benefit. The proposed system is developed with pretraind models are IIT Madras transformer ASR model and akashsivanandan/wav2vec2- large-xls-r-300m-tamil model. Both pretrained models are used to evaluate the test speech ut- terances, and obtainted the WER as 37.7144{\%} and 40.55{\%} respectively.
ASR\_SSN\_CSE@LTEDI- 2023: Pretrained Transformer based Automatic Speech Recognition system for Elderly People (2023)

Submission of the paper for the result submitted in Shared Task on Speech Recognition for Vulnerable Individuals in Tamil- LT-EDI-2023. The task is to develop an automatic speech recognition system for Tamil language. The dataset provided in the task is collected from the elderly people who converse in Tamil language. The proposed ASR system is designed with pre-trained model. The pre-trained model used in our system is fine-tuned with Tamil common voice dataset. The test data released from the task is given to the proposed system, now the transcriptions are generated for the test samples and the generated transcriptions is submitted to the task. The result submitted is evaluated by task, the evaluation metric used is Word Error Rate (WER). Our Proposed system attained a WER of 39.8091{\%}.
SSNTech2@LT-EDI-2023: Homophobia/Transphobia Detection in Social Media Comments Using Linear Classification Techniques (2023)

The abusive content on social media networks is causing destructive effects on the mental well-being of online users. Homophobia refers to the fear, negative attitudes and feeling towards homosexuality. Transphobia refer to negative attitudes, hatred and prejudice towards transsexual people. Even though, some parts of the society have started to accept homosexuality and transsexuality, there are still a large set of the population opposing it. Hate speech targeting LGBTQ+ individuals, known as homophobia/transphobia speech, has become a growing concern. This has led to a toxic and unwelcoming environment for LGBTQ+ people on online platforms. This poses a significant societal issue, hindering the progress of equality, diversity, and inclusion. The identification of homophobic and transphobic comments on social media platforms plays a crucial role in creating a safer environment for all social media users. In order to accomplish this, we built a machine learning model using SGD and SVM classifier. Our approach yielded promising results, with a weighted F1-score of 0.95 on the English dataset and we secured 4th rank in this task.
IJS@LT-EDI : Ensemble Approaches to Detect Signs of Depression from Social Media Text (2023)

This paper presents our ensembling solutions for detecting signs of depression in social media text, as part of the Shared Task at LT-EDI@RANLP 2023. By leveraging social media posts in English, the task involves the development of a system to accurately classify them as presenting signs of depression of one of three levels: {\textquotedblleft}severe{\textquotedblright}, {\textquotedblleft}moderate{\textquotedblright}, and {\textquotedblleft}not depressed{\textquotedblright}. We verify the hypothesis that combining contextual information from a language model with local domain-specific features can improve the classifier`s performance. We do so by evaluating: (1) two global classifiers (support vector machine and logistic regression); (2) contextual information from language models; and (3) the ensembling results.
VEL@LT-EDI-2023: Automatic Detection of Hope Speech in Bulgarian Language using Embedding Techniques (2023)

Many people may find motivation in their lives by spreading content on social media that is encouraging or hopeful. Creating an effective model that helps in accurately predicting the target class is a challenging task. The problem of Hope speech identification is dealt with in this work using machine learning and deep learning methods. This paper presents the description of the system submitted by our team(VEL) to the Hope Speech Detection for Equality, Diversity, and Inclusion(HSD-EDI) LT-EDI-RANLP 2023 shared task for the Bulgarian language. The main goal of this shared task is to identify the given text into the Hope speech or Non-Hope speech category. The proposed method used the H2O deep learning model with MPNet embeddings and achieved the second rank for the Bulgarian language with the Macro F1 score of 0.69.
Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding (2023)

Detecting transphobia, homophobia, and various other forms of hate speech is difficult. Signals can vary depending on factors such as language, culture, geographical region, and the particular online platform. Here, we present a joint multilingual (M-L) and language-specific (L-S) approach to homophobia and transphobic hate speech detection (HSD). M-L models are needed to catch words, phrases, and concepts that are less common or missing in a particular language and subsequently overlooked by L-S models. Nonetheless, L-S models are better situated to understand the cultural and linguistic context of the users who typically write in a particular language. Here we construct a simple and successful way to merge the M-L and L-S approaches through simple weight interpolation in such a way that is interpretable and data-driven. We demonstrate our system on task A of the {\textquotedblleft}Shared Task on Homophobia/Transphobia Detection in social media comments{\textquotedblright} dataset for homophobia and transphobic HSD. Our system achieves the best results in three of five languages and achieves a 0.997 macro average F1-score on Malayalam texts.
Cordyceps@LT-EDI : Depression Detection with Reddit and Self-training (2023)

Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. Given that there is a large number of people with excessive social media usage, then there is a significant population of potentially undiagnosed users and posts that they create. In this paper, we propose a depression detection system using a semi-supervised learning technique. Namely, we use a trained model to classify a large number of unlabelled social media posts from Reddit, then use these generated labels to train a more powerful classifier. We demonstrate our framework on Detecting Signs of Depression from Social Media Text - LT-EDI@RANLP 2023 shared task, where our framework ranks 3rd overall.
TechWhiz@LT-EDI-2023: Transformer Models to Detect Levels of Depression from Social Media Text (2023)

Depression is a mental fitness disorder from persistent reactions of unhappiness, void, and a deficit of interest in activities. It can influence differing facets of one`s life, containing their hopes, sympathy, and nature. Depression can stem from a sort of determinant, in the way that ancestral willingness, life occurrences, and social circumstances. In current years, the influence of social media on mental fitness has become an increasing concern. Excessive use of social media and the negative facets that guide it, can exacerbate or cause impressions of distress. The nonstop exposure to cautiously curated lives, social comparison, cyberbullying, and the pressure to meet unreal standards can impact an individual`s pride, social connections, and overall well-being. We participated in the shared task at DepSignLT-EDI@RANLP 2023 and have proposed a model that identifies the levels of depression from social media text using the data set shared for the task. Different transformer models like ALBERT and RoBERTa are used by the proposed model for implementing the task. The macro F1 score obtained by ALBERT model and RoBERTa model are 0.258 and 0.143 respectively.
CSE\_SPEECH@LT-EDI-2023Automatic Speech Recognition vulnerable old-aged and transgender people in Tamil (2023)

This paper centers on utilizing Automatic Speech Recognition (ASR) for defenseless old-aged and transgender people in Tamil. The Amrrs/wav2vec2-large-xlsr-53-tamil show accomplishes a Word Error Rate (WER) of 40{\%}. By leveraging this demonstration, ASR innovation upgrades availability and inclusivity, helping those with discourse impedances, hearing impedances, and cognitive inabilities. Assist refinements are vital to diminish error and move forward the client involvement. This inquiry emphasizes the significance of ASR, particularly the Amrrs/wav2vec2-large-xlsr-53-tamil show, in encouraging successful communication and availability for defenseless populaces in Tamil.
VTUBGM@LT-EDI-2023: Hope Speech Identification using Layered Differential Training of ULMFit (2023)

Hope speech embodies optimistic and uplifting sentiments, aiming to inspire individuals to maintain faith in positive progress and actively contribute to a better future. In this article, we outline the model presented by our team, VTUBGM, for the shared task {\textquotedblleft}Hope Speech Detection for Equality, Diversity, and Inclusion{\textquotedblright} at LT-EDI-RANLP 2023. This task entails classifying YouTube comments, which is a classification problem at the comment level. The task was conducted in four different languages: Bulgarian, English, Hindi, and Spanish. VTUBGM submitted a model developed through layered differential training of the ULMFit model. As a result, a macro F1 score of 0.48 was obtained and ranked 3rd in the competition.