pwc-archive/papers-with-abstracts huggingface dataset:
IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning
Authors: Pradeep Roy, Snehaan Bhawal, Abhinav Kumar, Bharathi Raja Chakravarthi
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: BIG-bench Machine Learning, Diversity, Hope Speech Detection

This paper addresses the issue of Hope Speech detection using machine learning techniques. Designing a robust model that helps in predicting the target class with higher accuracy is a challenging task in machine learning, especially when the distribution of the class labels is highly imbalanced. This study uses and compares the experimental outcomes of the different oversampling techniques. Many models are implemented to classify the comments into Hope and Non-Hope speech, and it found that machine learning algorithms perform better than deep learning models. The English language dataset used in this research was developed by collecting YouTube comments and is part of the task “ACL-2022:Hope Speech Detection for Equality, Diversity, and Inclusion”. The proposed model achieved a weighted F1-score of 0.55 on the test dataset and secured the first rank among the participated teams.
The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada
Authors: Adeep Hande, Siddhanth U Hegde, Sangeetha S, Ruba Priyadharshini, Bharathi Raja Chakravarthi
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Cross-Lingual Transfer, Hope Speech Detection, Language Modeling, Language Modelling

In recent years, various methods have been developed to control the spread of negativity by removing profane, aggressive, and offensive comments from social media platforms. There is, however, a scarcity of research focusing on embracing positivity and reinforcing supportive and reassuring content in online forums. As a result, we concentrate our research on developing systems to detect hope speech in code-mixed Kannada. As a result, we present DC-LM, a dual-channel language model that sees hope speech by using the English translations of the code-mixed dataset for additional training. The approach is jointly modelled on both English and code-mixed Kannada to enable effective cross-lingual transfer between the languages. With a weighted F1-score of 0.756, the method outperforms other models. We aim to initiate research in Kannada while encouraging researchers to take a pragmatic approach to inspire positive and supportive online content.
NYCU_TWD@LT-EDI-ACL2022: Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media
Authors: Wei-Yao Wang, Yu-Chien Tang, Wei-Wei Du, Wen-Chih Peng
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Contrastive Learning

This paper presents a state-of-the-art solution to the LT-EDI-ACL 2022 Task 4: Detecting Signs of Depression from Social Media Text. The goal of this task is to detect the severity levels of depression of people from social media posts, where people often share their feelings on a daily basis. To detect the signs of depression, we propose a framework with pre-trained language models using rich information instead of training from scratch, gradient boosting and deep learning models for modeling various aspects, and supervised contrastive learning for the generalization ability. Moreover, ensemble techniques are also employed in consideration of the different advantages of each method. Experiments show that our framework achieves a 2nd prize ranking with a macro F1-score of 0.552, showing the effectiveness and robustness of our approach.
UMUTeam@LT-EDI-ACL2022: Detecting homophobic and transphobic comments in Tamil
Authors: José García-Díaz, Camilo Caparros-Laiz, Rafael Valencia-García
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Sentence, Sentence Embeddings, Transliteration

This working-notes are about the participation of the UMUTeam in a LT-EDI shared task concerning the identification of homophobic and transphobic comments in YouTube. These comments are written in English, which has high availability to machine-learning resources; Tamil, which has fewer resources; and a transliteration from Tamil to Roman script combined with English sentences. To carry out this shared task, we train a neural network that combines several feature sets applying a knowledge integration strategy. These features are linguistic features extracted from a tool developed by our research group and contextual and non-contextual sentence embeddings. We ranked 7th for English subtask (macro f1-score of 45%), 3rd for Tamil subtask (macro f1-score of 82%), and 2nd for Tamil-English subtask (macro f1-score of 58%).
UMUTeam@LT-EDI-ACL2022: Detecting Signs of Depression from text
Authors: José García-Díaz, Rafael Valencia-García
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Emotion Recognition, Position, Sentence, Sentence Embeddings

Depression is a mental condition related to sadness and the lack of interest in common daily tasks. In this working-notes, we describe the proposal of the UMUTeam in the LT-EDI shared task (ACL 2022) concerning the identification of signs of depression in social network posts. This task is somehow related to other relevant Natural Language Processing tasks such as Emotion Analysis. In this shared task, the organisers challenged the participants to distinguish between moderate and severe signs of depression (or no signs of depression at all) in a set of social posts written in English. Our proposal is based on the combination of linguistic features and several sentence embeddings using a knowledge integration strategy. Our proposal achieved the 6th position, with a macro f1-score of 53.82 in the official leader board.
ABLIMET @LT-EDI-ACL2022: A Roberta based Approach for Homophobia/Transphobia Detection in Social Media
Authors: Abulimiti Maimaitituoheti
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5

This paper describes our system that participated in LT-EDI-ACL2022- Homophobia/Transphobia Detection in Social Media. Sexual minorities face a lot of unfair treatment and discrimination in our world. This creates enormous stress and many psychological problems for sexual minorities. There is a lot of hate speech on the internet, and Homophobia/Transphobia is the one against sexual minorities. Identifying and processing Homophobia/ Transphobia through natural language processing technology can improve the efficiency of processing Homophobia/ Transphobia, and can quickly screen out Homophobia/Transphobia on the Internet. The organizer of LT-EDI-ACL2022- Homophobia/Transphobia Detection in Social Media constructs a Homophobia/ Transphobia detection dataset based on YouTube comments for English and Tamil. We use a Roberta -based approach to conduct Homophobia/ Transphobia detection experiments on the dataset of the competition, and get better results.
MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM
Authors: Anusha Gowda, Fazlourrahman Balouchzahi, Hosahalli Shashirekha, Grigori Sidorov
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection

Spreading positive vibes or hope content on social media may help many people to get motivated in their life. To address Hope Speech detection in YouTube comments, this paper presents the description of the models submitted by our team - MUCIC, to the Hope Speech Detection for Equality, Diversity, and Inclusion (HopeEDI) shared task at Association for Computational Linguistics (ACL) 2022. This shared task consists of texts in five languages, namely: English, Spanish (in Latin scripts), and Tamil, Malayalam, and Kannada (in code-mixed native and Roman scripts) with the aim of classifying the YouTube comment into “Hope”, “Not-Hope” or “Not-Intended” categories. The proposed methodology uses the re-sampling technique to deal with imbalanced data in the corpus and obtained 1st rank for English language with a macro-averaged F1-score of 0.550 and weighted-averaged F1-score of 0.860. The code to reproduce this work is available in GitHub.
DeepBlues@LT-EDI-ACL2022: Depression level detection modelling through domain specific BERT and short text Depression classifiers
Authors: Nawshad Farruque, Osmar Zaiane, Randy Goebel, Sudhakar Sivapalan
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5

We discuss a variety of approaches to build a robust Depression level detection model from longer social media posts (i.e., Reddit Depression forum posts) using a mental health text pre-trained BERT model. Further, we report our experimental results based on a strategy to select excerpts from long text and then fine-tune the BERT model to combat the issue of memory constraints while processing such texts. We show that, with domain specific BERT, we can achieve reasonable accuracy with fixed text size (in this case 200 tokens) for this task. In addition we can use short text classifiers to extract relevant text from the long text and achieve slightly better accuracy, albeit, trading off with the processing time for extracting such excerpts.
SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model
Authors: Praveenkumar Vijayakumar, Prathyush S, Aravind P, Angel S, Rajalakshmi Sivanaiah, Sakaya Milton Rajendram, Mirnalinee T T
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection, Language Modelling, Sentence

In recent years social media has become one of the major forums for expressing human views and emotions. With the help of smartphones and high-speed internet, anyone can express their views on Social media. However, this can also lead to the spread of hatred and violence in society. Therefore it is necessary to build a method to find and support helpful social media content. In this paper, we studied Natural Language Processing approach for detecting Hope speech in a given sentence. The task was to classify the sentences into ‘Hope speech’ and ‘Non-hope speech’. The dataset was provided by LT-EDI organizers with text from Youtube comments. Based on the task description, we developed a system using the pre-trained language model BERT to complete this task. Our model achieved 1st rank in the Kannada language with a weighted average F1 score of 0.750, 2nd rank in the Malayalam language with a weighted average F1 score of 0.740, 3rd rank in the Tamil language with a weighted average F1 score of 0.390 and 6th rank in the English language with a weighted average F1 score of 0.880.
SUH_ASR@LT-EDI-ACL2022: Transformer based Approach for Speech Recognition for Vulnerable Individuals in Tamil
Authors: Suhasini S, Bharathi 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

An Automatic Speech Recognition System is developed for addressing the Tamil conversational speech data of the elderly people andtransgender. The speech corpus used in this system is collected from the people who adhere their communication in Tamil at some primary places like bank, hospital, vegetable markets. Our ASR system is designed with pre-trained model which is used to recognize the speechdata. WER(Word Error Rate) calculation is used to analyse the performance of the ASR system. This evaluation could help to make acomparison of utterances between the elderly people and others. Similarly, the comparison between the transgender and other people isalso done. Our proposed ASR system achieves the word error rate as 39.65%.
LPS@LT-EDI-ACL2022:An Ensemble Approach about Hope Speech Detection
Authors: Yue Zhu
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection

The task shared by sponsor about Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI-ACL-2022.The goal of this task is to identify whether a given comment contains hope speech or not,and hope is considered significant for the well-being, recuperation and restoration of human life.Our work aims to change the prevalent way of thinking by moving away from a preoccupation with discrimination, loneliness or the worst things in life to building the confidence, support and good qualities based on comments by individuals. In response to the need to detect equality, diversity and inclusion of hope speech in a multilingual environment, we built an integration model and achieved well performance on multiple datasets presented by the sponsor and the specific results can be referred to the experimental results section.
CURAJ_IIITDWD@LT-EDI-ACL 2022: Hope Speech Detection in English YouTube Comments using Deep Learning Techniques
Authors: Vanshita Jha, Ankit Mishra, Sunil Saumya
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection

Hope Speech are positive terms that help to promote or criticise a point of view without hurting the user’s or community’s feelings. Non-Hope Speech, on the other side, includes expressions that are harsh, ridiculing, or demotivating. The goal of this article is to find the hope speech comments in a YouTube dataset. The datasets were created as part of the “LT-EDI-ACL 2022: Hope Speech Detection for Equality, Diversity, and Inclusion” shared task. The shared task dataset was proposed in Malayalam, Tamil, English, Spanish, and Kannada languages. In this paper, we worked at English-language YouTube comments. We employed several deep learning based models such as DNN (dense or fully connected neural network), CNN (Convolutional Neural Network), Bi-LSTM (Bidirectional Long Short Term Memory Network), and GRU(Gated Recurrent Unit) to identify the hopeful comments. We also used Stacked LSTM-CNN and Stacked LSTM-LSTM network to train the model. The best macro average F1-score 0.67 for development dataset was obtained using the DNN model. The macro average F1-score of 0.67 was achieved for the classification done on the test data as well.
SSN_MLRG3 @LT-EDI-ACL2022-Depression Detection System from Social Media Text using Transformer Models
Authors: Sarika Esackimuthu, Shruthi Hariprasad, Rajalakshmi Sivanaiah, Angel S, Sakaya Milton Rajendram, Mirnalinee T T
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Depression Detection, Multi-class Classification

Depression is a common mental illness that involves sadness and lack of interest in all day-to-day activities. The task is to classify the social media text as signs of depression into three labels namely “not depressed”, “moderately depressed”, and “severely depressed”. We have build a system using Deep Learning Model “Transformers”. Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The multi-class classification model used in our system is based on the ALBERT model. In the shared task ACL 2022, Our team SSN_MLRG3 obtained a Macro F1 score of 0.473.
BERT 4EVER@LT-EDI-ACL2022-Detecting signs of Depression from Social Media:Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster
Authors: Xiaotian Lin, Yingwen Fu, Ziyu Yang, Nankai Lin, Shengyi Jiang
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Prompt Learning, text-classification, Text Classification, Text Generation, Translation

In this paper, we report the solution of the team BERT 4EVER for the LT-EDI-2022 shared task2: Homophobia/Transphobia Detection in social media comments in ACL 2022, which aims to classify Youtube comments into one of the following categories: no,moderate, or severe depression. We model the problem as a text classification task and a text generation task and respectively propose two different models for the tasks.To combine the knowledge learned from these two different models, we softly fuse the predicted probabilities of the models above and then select the label with the highest probability as the final output.In addition, multiple augmentation strategies are leveraged to improve the model generalization capability, such as back translation and adversarial training.Experimental results demonstrate the effectiveness of the proposed models and two augmented strategies.
CIC@LT-EDI-ACL2022: Are transformers the only hope? Hope speech detection for Spanish and English comments
Authors: Fazlourrahman Balouchzahi, Sabur Butt, Grigori Sidorov, Alexander Gelbukh
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Hope Speech Detection

Hope is an inherent part of human life and essential for improving the quality of life. Hope increases happiness and reduces stress and feelings of helplessness. Hope speech is the desired outcome for better and can be studied using text from various online sources where people express their desires and outcomes. In this paper, we address a deep-learning approach with a combination of linguistic and psycho-linguistic features for hope-speech detection. We report our best results submitted to LT-EDI-2022 which ranked 2nd and 3rd in English and Spanish respectively.
scubeMSEC@LT-EDI-ACL2022: Detection of Depression using Transformer Models
Authors: Sivamanikandan S, Santhosh V, Sanjaykumar N, Jerin Mahibha C, Thenmozhi Durairaj
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5

Social media platforms play a major role in our day-to-day life and are considered as a virtual friend by many users, who use the social media to share their feelings all day. Many a time, the content which is shared by users on social media replicate their internal life. Nowadays people love to share their daily life incidents like happy or unhappy moments and their feelings in social media and it makes them feel complete and it has become a habit for many users. Social media provides a new chance to identify the feelings of a person through their posts. The aim of the shared task is to develop a model in which the system is capable of analyzing the grammatical markers related to onset and permanent symptoms of depression. We as a team participated in the shared task Detecting Signs of Depression from Social Media Text at LT-EDI 2022- ACL 2022 and we have proposed a model which predicts depression from English social media posts using the data set shared for the task. The prediction is done based on the labels Moderate, Severe and Not Depressed. We have implemented this using different transformer models like DistilBERT, RoBERTa and ALBERT by which we were able to achieve a Macro F1 score of 0.337, 0.457 and 0.387 respectively. Our code is publicly available in the github
SSNCSE_NLP@LT-EDI-ACL2022:Hope Speech Detection for Equality, Diversity and Inclusion using sentence transformers
Authors: Bharathi B, Dhanya Srinivasan, Josephine Varsha, Thenmozhi Durairaj, Senthil Kumar B
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection, Sentence

In recent times, applications have been developed to regulate and control the spread of negativity and toxicity on online platforms. The world is filled with serious problems like political & religious conflicts, wars, pandemics, and offensive hate speech is the last thing we desire. Our task was to classify a text into ‘Hope Speech’ and ‘Non-Hope Speech’. We searched for datasets acquired from YouTube comments that offer support, reassurance, inspiration, and insight, and the ones that don’t. The datasets were provided to us by the LTEDI organizers in English, Tamil, Spanish, Kannada, and Malayalam. To successfully identify and classify them, we employed several machine learning transformer models such as m-BERT, MLNet, BERT, XLMRoberta, and XLM_MLM. The observed results indicate that the BERT and m-BERT have obtained the best results among all the other techniques, gaining a weighted F1- score of 0.92, 0.71, 0.76, 0.87, and 0.83 for English, Tamil, Spanish, Kannada, and Malayalam respectively. This paper depicts our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LTEDI 2021.
SOA_NLP@LT-EDI-ACL2022: An Ensemble Model for Hope Speech Detection from YouTube Comments
Authors: Abhinav Kumar, Sunil Saumya, Pradeep Roy
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection, regression

Language should be accommodating of equality and diversity as a fundamental aspect of communication. The language of internet users has a big impact on peer users all over the world. On virtual platforms such as Facebook, Twitter, and YouTube, people express their opinions in different languages. People respect others’ accomplishments, pray for their well-being, and cheer them on when they fail. Such motivational remarks are hope speech remarks. Simultaneously, a group of users encourages discrimination against women, people of color, people with disabilities, and other minorities based on gender, race, sexual orientation, and other factors. To recognize hope speech from YouTube comments, the current study offers an ensemble approach that combines a support vector machine, logistic regression, and random forest classifiers. Extensive testing was carried out to discover the best features for the aforementioned classifiers. In the support vector machine and logistic regression classifiers, char-level TF-IDF features were used, whereas in the random forest classifier, word-level features were used. The proposed ensemble model performed significantly well among English, Spanish, Tamil, Malayalam, and Kannada YouTube comments.
IIT Dhanbad @LT-EDI-ACL2022- Hope Speech Detection for Equality, Diversity, and Inclusion
Authors: Vishesh Gupta, Ritesh Kumar, Rajendra Pamula
Date: 2022-05-01 00:00:00
Proceeding: LTEDI (ACL) 2022 5
Tasks: Diversity, Hope Speech Detection

Hope is considered significant for the wellbeing,recuperation and restoration of humanlife by health professionals. Hope speech reflectsthe belief that one can discover pathwaysto their desired objectives and become rousedto utilise those pathways. Hope speech offerssupport, reassurance, suggestions, inspirationand insight. Hate speech is a prevalent practicethat society has to struggle with everyday.The freedom of speech and ease of anonymitygranted by social media has also resulted inincitement to hatred. In this paper, we workto identify and promote positive and supportivecontent on these platforms. We work withseveral machine learning models to classify socialmedia comments as hope speech or nonhopespeech in English. This paper portraysour work for the Shared Task on Hope SpeechDetection for Equality, Diversity, and Inclusionat LT-EDI-ACL 2022.