pwc-archive/papers-with-abstracts huggingface dataset:
IISERB@LT-EDI-ACL2022: A Bag of Words and Document Embeddings Based Framework to Identify Severity of Depression Over Social Media
Authors: Tanmay Basu
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: text-classification, Text Classification

The DepSign-LT-EDI-ACL2022 shared task focuses on early prediction of severity of depression over social media posts. The BioNLP group at Department of Data Science and Engineering in Indian Institute of Science Education and Research Bhopal (IISERB) has participated in this challenge and submitted three runs based on three different text mining models. The severity of depression were categorized into three classes, viz., no depression, moderate, and severe and the data to build models were released as part of this shared task. The objective of this work is to identify relevant features from the given social media texts for effective text classification. As part of our investigation, we explored features derived from text data using document embeddings technique and simple bag of words model following different weighting schemes. Subsequently, adaptive boosting, logistic regression, random forest and support vector machine (SVM) classifiers were used to identify the scale of depression from the given texts. The experimental analysis on the given validation data show that the SVM classifier using the bag of words model following term frequency and inverse document frequency weighting scheme outperforms the other models for identifying depression. However, this framework could not achieve a place among the top ten runs of the shared task. This paper describes the potential of the proposed framework as well as the possible reasons behind mediocre performance on the given data.
SSNCSE_NLP@LT-EDI-ACL2022: Homophobia/Transphobia Detection in Multiple Languages using SVM Classifiers and BERT-based Transformers
Authors: Krithika Swaminathan, Bharathi B, Gayathri G L, Hrishik Sampath
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Word Embeddings

Over the years, there has been a slow but steady change in the attitude of society towards different kinds of sexuality. However, on social media platforms, where people have the license to be anonymous, toxic comments targeted at homosexuals, transgenders and the LGBTQ+ community are not uncommon. Detection of homophobic comments on social media can be useful in making the internet a safer place for everyone. For this task, we used a combination of word embeddings and SVM Classifiers as well as some BERT-based transformers. We achieved a weighted F1-score of 0.93 on the English dataset, 0.75 on the Tamil dataset and 0.87 on the Tamil-English Code-Mixed dataset.
E8-IJS@LT-EDI-ACL2022 - BERT, AutoML and Knowledge-graph backed Detection of Depression
Authors: Ilija Tavchioski, Boshko Koloski, Blaž Škrlj, Senja Pollak
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: AutoML

Depression is a mental illness that negatively affects a person’s well-being and can, if left untreated, lead to serious consequences such as suicide. Therefore, it is important to recognize the signs of depression early. In the last decade, social media has become one of the most common places to express one’s feelings. Hence, there is a possibility of text processing and applying machine learning techniques to detect possible signs of depression. In this paper, we present our approaches to solving the shared task titled Detecting Signs of Depression from Social Media Text. We explore three different approaches to solve the challenge: fine-tuning BERT model, leveraging AutoML for the construction of features and classifier selection and finally, we explore latent spaces derived from the combination of textual and knowledge-based representations. We ranked 9th out of 31 teams in the competition. Our best solution, based on knowledge graph and textual representations, was 4.9% behind the best model in terms of Macro F1, and only 1.9% behind in terms of Recall.
Nozza@LT-EDI-ACL2022: Ensemble Modeling for Homophobia and Transphobia Detection
Authors: Debora Nozza
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Data Augmentation, Position

In this paper, we describe our approach for the task of homophobia and transphobia detection in English social media comments. The dataset consists of YouTube comments, and it has been released for the shared task on Homophobia/Transphobia Detection in social media comments. Given the high class imbalance, we propose a solution based on data augmentation and ensemble modeling. We fine-tuned different large language models (BERT, RoBERTa, and HateBERT) and used the weighted majority vote on their predictions.Our proposed model obtained 0.48 and 0.94 for macro and weighted F1-score, respectively, ranking at the third position.
KADO@LT-EDI-ACL2022: BERT-based Ensembles for Detecting Signs of Depression from Social Media Text
Authors: Morteza Janatdoust, Fatemeh Ehsani-Besheli, Hossein Zeinali
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5

Depression is a common and serious mental illness that early detection can improve the patient’s symptoms and make depression easier to treat. This paper mainly introduces the relevant content of the task “Detecting Signs of Depression from Social Media Text at DepSign-LT-EDI@ACL-2022”. The goal of DepSign is to classify the signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed” based on social media’s posts. In this paper, we propose a predictive ensemble model that utilizes the fine-tuned contextualized word embedding, ALBERT, DistilBERT, RoBERTa, and BERT base model. We show that our model outperforms the baseline models in all considered metrics and achieves an F1 score of 54% and accuracy of 61%, ranking 5th on the leader-board for the DepSign task.
Sammaan@LT-EDI-ACL2022: Ensembled Transformers Against Homophobia and Transphobia
Authors: Ishan Sanjeev Upadhyay, KV Aditya Srivatsa, Radhika Mamidi
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity

Hateful and offensive content on social media platforms can have negative effects on users and can make online communities more hostile towards certain people and hamper equality, diversity and inclusion. In this paper, we describe our approach to classify homophobia and transphobia in social media comments. We used an ensemble of transformer-based models to build our classifier. Our model ranked 2nd for English, 8th for Tamil and 10th for Tamil-English.
OPI@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text using RoBERTa Pre-trained Language Models
Authors: Rafał Poświata, Michał Perełkiewicz
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Depression Detection, Language Modeling, Language Modelling

This paper presents our winning solution for the Shared Task on Detecting Signs of Depression from Social Media Text at LT-EDI-ACL2022. The task was to create a system that, given social media posts in English, should detect the level of depression as ‘not depressed’, ‘moderately depressed’ or ‘severely depressed’. We based our solution on transformer-based language models. We fine-tuned selected models: BERT, RoBERTa, XLNet, of which the best results were obtained for RoBERTa. Then, using the prepared corpus, we trained our own language model called DepRoBERTa (RoBERTa for Depression Detection). Fine-tuning of this model improved the results. The third solution was to use the ensemble averaging, which turned out to be the best solution. It achieved a macro-averaged F1-score of 0.583. The source code of prepared solution is available at https://github.com/rafalposwiata/depression-detection-lt-edi-2022.
FilipN@LT-EDI-ACL2022-Detecting signs of Depression from Social Media: Examining the use of summarization methods as data augmentation for text classification
Authors: Filip Nilsson, György Kovács
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Abstractive Text Summarization, Classification, Data Augmentation, Depression Detection, text-classification, Text Classification

Depression is a common mental disorder that severely affects the quality of life, and can lead to suicide. When diagnosed in time, mild, moderate, and even severe depression can be treated. This is why it is vital to detect signs of depression in time. One possibility for this is the use of text classification models on social media posts. Transformers have achieved state-of-the-art performance on a variety of similar text classification tasks. One drawback, however, is that when the dataset is imbalanced, the performance of these models may be negatively affected. Because of this, in this paper, we examine the effect of balancing a depression detection dataset using data augmentation. In particular, we use abstractive summarization techniques for data augmentation. We examine the effect of this method on the LT-EDI-ACL2022 task. Our results show that when increasing the multiplicity of the minority classes to the right degree, this data augmentation method can in fact improve classification scores on the task.
NAYEL @LT-EDI-ACL2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM
Authors: Nsrin Ashraf, Mohamed Taha, Ahmed Abd Elfattah, Hamada Nayel
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity

Analysing the contents of social media platforms such as YouTube, Facebook and Twitter gained interest due to the vast number of users. One of the important tasks is homophobia/transphobia detection. This paper illustrates the system submitted by our team for the homophobia/transphobia detection in social media comments shared task. A machine learning-based model has been designed and various classification algorithms have been implemented for automatic detection of homophobia in YouTube comments. TF/IDF has been used with a range of bigram model for vectorization of comments. Support Vector Machines has been used to develop the proposed model and our submission reported 0.91, 0.92, 0.88 weighted f1-score for English, Tamil and Tamil-English datasets respectively.
giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI
Authors: Harshul Surana, Basavraj Chinagundi
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Decoder, Diversity, Hope Speech Detection

This paper describes team giniUs’ submission to the Hope Speech Detection for Equality, Diversity and Inclusion Shared Task organised by LT-EDI ACL 2022. We have fine-tuned the Roberta-large pre-trained model and extracted the last four decoder layers to build a classifier. Our best result on the leaderboard achieve a weighted F1 score of 0.86 and a Macro F1 score of 0.51 for English. We have secured a rank of 4 for the English task. We have open-sourced our code implementations on GitHub to facilitate easy reproducibility by the scientific community.
SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text
Authors: Karun Anantharaman, Angel S, Rajalakshmi Sivanaiah, Saritha Madhavan, Sakaya Milton Rajendram
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Multi-class Classification

DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely “not depressed”,“moderately depressed”, and “severely de-pressed”. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN_MLRG1 achieves 58.5%accuracy on the DepSign-LT-EDI@ACL-2022test set.
DepressionOne@LT-EDI-ACL2022: Using Machine Learning with SMOTE and Random UnderSampling to Detect Signs of Depression on Social Media Text.
Authors: Suman Dowlagar, Radhika Mamidi
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5

Depression is a common and serious medical illness that negatively affects how you feel, the way you think, and how you act. Detecting depression is essential as it must be treated early to avoid painful consequences. Nowadays, people are broadcasting how they feel via posts and comments. Using social media, we can extract many comments related to depression and use NLP techniques to train and detect depression. This work presents the submission of the DepressionOne team at LT-EDI-2022 for the shared task, detecting signs of depression from social media text. The depression data is small and unbalanced. Thus, we have used oversampling and undersampling methods such as SMOTE and RandomUnderSampler to represent the data. Later, we used machine learning methods to train and detect the signs of depression.
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide
Authors: Arianna Muti, Marta Marchiori Manerba, Katerina Korre, Alberto Barrón-Cedeño
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Active Learning, Diversity, Hope Speech Detection

The 2022 edition of LT-EDI proposed two tasks in various languages. Task Hope Speech Detection required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. Task Homophobia/Transphobia Detection focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task i and applied it to the dataset for Task j to iteratively integrate new silver data for Task i. Our official submissions to the shared task obtained a macro-averaged F_1 score of 0.53 for Hope Speech and 0.46 for Homo/Transphobia, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.
MUCS@Text-LT-EDI@ACL 2022: Detecting Sign of Depression from Social Media Text using Supervised Learning Approach
Authors: Asha Hegde, Sharal Coelho, Ahmad Elyas Dashti, Hosahalli Shashirekha
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Transfer Learning

Social media has seen enormous growth in its users recently and knowingly or unknowingly the behavior of a person will be reflected in the comments she/he posts on social media. Users having the sign of depression may post negative or disturbing content seeking the attention of other users. Hence, social media data can be analysed to check whether the users’ have the sign of depression and help them to get through the situation if required. However, as analyzing the increasing amount of social media data manually in laborious and error-prone, automated tools have to be developed for the same. To address the issue of detecting the sign of depression content on social media, in this paper, we - team MUCS, describe an Ensemble of Machine Learning (ML) models and a Transfer Learning (TL) model submitted to “Detecting Signs of Depression from Social Media Text-LT-EDI@ACL 2022” (DepSign-LT-EDI@ACL-2022) shared task at Association for Computational Linguistics (ACL) 2022. Both frequency and text based features are used to train an Ensemble model and Bidirectional Encoder Representations from Transformers (BERT) fine-tuned with raw text is used to train the TL model. Among the two models, the TL model performed better with a macro averaged F-score of 0.479 and placed 18th rank in the shared task. The code to reproduce the proposed models is available in github page1.
SSNCSE_NLP@LT-EDI-ACL2022: Speech Recognition for Vulnerable Individuals in Tamil using pre-trained XLSR models
Authors: Dhanya Srinivasan, Bharathi B, Thenmozhi Durairaj, Senthil Kumar B
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition is a tool used to transform human speech into a written form. It is used in a variety of avenues, such as in voice commands, customer, service and more. It has emerged as an essential tool in the digitisation of daily life. It has been known to be of vital importance in making the lives of elderly and disabled people much easier. In this paper we describe an automatic speech recognition model, determined by using three pre-trained models, fine-tuned from the Facebook XLSR Wav2Vec2 model, which was trained using the Common Voice Dataset. The best model for speech recognition in Tamil is determined by finding the word error rate of the data. This work explains the submission made by SSNCSE_NLP in the shared task organized by LT-EDI at ACL 2022. A word error rate of 39.4512 is achieved.
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention Mechanism
Authors: Deepanshu Khanna, Muskaan Singh, Petr Motlicek
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection

With the increase of users on social media platforms, manipulating or provoking masses of people has become a piece of cake. This spread of hatred among people, which has become a loophole for freedom of speech, must be minimized. Hence, it is essential to have a system that automatically classifies the hatred content, especially on social media, to take it down. This paper presents a simple modular pipeline classifier with BERT embeddings and attention mechanism to classify hope speech content in the Hope Speech Detection shared task for Equality, Diversity, and Inclusion-ACL 2022. Our system submission ranks fourth with an F1-score of 0.84. We release our code-base here https://github.com/Deepanshu-beep/hope-speech-attention .
SSN@LT-EDI-ACL2022: Transfer Learning using BERT for Detecting Signs of Depression from Social Media Texts
Authors: Adarsh S, Betina Antony
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Transfer Learning

Depression is one of the most common mentalissues faced by people. Detecting signs ofdepression early on can help in the treatmentand prevention of extreme outcomes like suicide.Since the advent of the internet, peoplehave felt more comfortable discussing topicslike depression online due to the anonymityit provides. This shared task has used datascraped from various social media sites andaims to develop models that detect signs andthe severity of depression effectively. In thispaper, we employ transfer learning by applyingenhanced BERT model trained for Wikipediadataset to the social media text and performtext classification. The model gives a F1-scoreof 63.8% which was reasonably better than theother competing models.
Findings of the Shared Task on Detecting Signs of Depression from Social Media
Authors: Kayalvizhi S, Thenmozhi Durairaj, Bharathi Raja Chakravarthi, Jerin Mahibha C
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5

Social media is considered as a platform whereusers express themselves. The rise of social me-dia as one of humanity’s most important publiccommunication platforms presents a potentialprospect for early identification and manage-ment of mental illness. Depression is one suchillness that can lead to a variety of emotionaland physical problems. It is necessary to mea-sure the level of depression from the socialmedia text to treat them and to avoid the nega-tive consequences. Detecting levels of depres-sion is a challenging task since it involves themindset of the people which can change period-ically. The aim of the DepSign-LT-EDI@ACL-2022 shared task is to classify the social me-dia text into three levels of depression namely“Not Depressed”, “Moderately Depressed”, and“Severely Depressed”. This overview presentsa description on the task, the data set, method-ologies used and an analysis on the results ofthe submissions. The models that were submit-ted as a part of the shared task had used a va-riety of technologies from traditional machinelearning algorithms to deep learning models.It could be observed from the result that thetransformer based models have outperformedthe other models. Among the 31 teams whohad submitted their results for the shared task,the best macro F1-score of 0.583 was obtainedusing transformer based model.
Findings of the Shared Task on Speech Recognition for Vulnerable Individuals in Tamil
Authors: Bharathi B, Bharathi Raja Chakravarthi, Subalalitha Cn, Sripriya N, Arunaggiri Pandian, Swetha Valli
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper illustrates the overview of the sharedtask on automatic speech recognition in the Tamillanguage. In the shared task, spontaneousTamil speech data gathered from elderly andtransgender people was given for recognitionand evaluation. These utterances were collected from people when they communicatedin the public locations such as hospitals, markets, vegetable shop, etc. The speech corpusincludes utterances of male, female, and transgender and was split into training and testingdata. The given task was evaluated using WER(Word Error Rate). The participants used thetransformer-based model for automatic speechrecognition. Different results using differentpre-trained transformer models are discussedin this overview paper.
DLRG@LT-EDI-ACL2022:Detecting signs of Depression from Social Media using XGBoost Method
Authors: Herbert Sharen, Ratnavel Rajalakshmi
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5

Depression is linked to the development of dementia.Cognitive functions such as thinkingand remembering generally deteriorate in dementiapatients. Social media usage has beenincreased among the people in recent days. Thetechnology advancements help the communityto express their views publicly. Analysing thesigns of depression from texts has become animportant area of research now, as it helps toidentify this kind of mental disorders among thepeople from their social media posts. As part ofthe shared task on detecting signs of depressionfrom social media text, a dataset has been providedby the organizers (Sampath et al.). Weapplied different machine learning techniquessuch as Support Vector Machine, Random Forestand XGBoost classifier to classify the signsof depression. Experimental results revealedthat, the XGBoost model outperformed othermodels with the highest classification accuracyof 0.61% and an Macro F1 score of 0.54.
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and Inclusion
Authors: Muskaan Singh, Petr Motlicek
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection

Social media platforms have been provoking masses of people. The individual comments affect a prevalent way of thinking by moving away from preoccupation with discrimination, loneliness, or influence in building confidence, support, and good qualities. This paper aims to identify hope in these social media posts. Hope significantly impacts the well-being of people, as suggested by health professionals. It reflects the belief to achieve an objective, discovers a new path, or become motivated to formulate pathways.In this paper we classify given a social media post, hope speech or not hope speech, using ensembled voting of BERT, ERNIE 2.0 and RoBERTa for English language with 0.54 macro F1-score (2^{st} rank). For non-English languages Malayalam, Spanish and Tamil we utilized XLM RoBERTA with 0.50, 0.81, 0.3 macro F1 score (1^{st}, 1^{st},3^{rd} rank) respectively. For Kannada, we use Multilingual BERT with 0.32 F1 score(5^{th})position. We release our code-base here: https://github.com/Muskaan-Singh/Hate-Speech-detection.git.