Swahili, spoken by around 200 million people primarily in Tanzania and Kenya, has been the focus of our research for the GEM Shared Task at INLG’24 on Underrepresented Language Summarization. We have utilized the XLSUM dataset and have manually summarized 1000 texts from a Swahili news classification dataset. To achieve the desired results, we have tested abstractive summarizers (mT5_multilingual_XLSum, t5-small, mBART-50), and an extractive summarizer (based on PageRank algorithm). But our adopted model consists of an integrated extractive-abstractive model combining the Bert Extractive Summarizer with some abstractive summarizers (t5-small, mBART-50). The integrated model overcome the drawbacks of both the extractive and abstractive summarization system and utilizes the benefit from both of it. Extractive summarizer shorten the paragraphs exceeding 512 tokens, ensuring no important information has been lost before applying the abstractive models. The abstractive summarizer use its pretrained knowledge and ensure to generate context based summary.

In this paper, we have addressed Task 3 on social anxiety disorder identification and Task 5 on mental illness recognition organized by the SMM4H 2024 workshop. In Task 3, a multi-classification problem has been presented to classify Reddit posts about outdoor spaces into four categories: Positive, Neutral, Negative, or Unrelated. Using the pre-trained RoBERTa-base model along with techniques like Mean pooling, CLS, and Attention Head, we have scored an F1-Score of 0.596 on the test dataset for Task 3. Task 5 aims to classify tweets into two categories: those describing a child with conditions like ADHD, ASD, delayed speech, or asthma (class 1), and those merely mentioning a disorder (class 0). Using the pre-trained RoBERTa-large model, incorporating a weighted ensemble of the last 4 hidden layers through concatenation and mean pooling, we achieved an F1 Score of 0.928 on the test data for Task 5.

In the financial industry, identifying user intent from text inputs is crucial for various tasks such as automated trading, sentiment analysis, and customer support. One important component of natural language processing (NLP) is intent detection, which is significant to the finance sector. Limited studies have been conducted in the field of finance using languages with limited resources like Arabic, despite notable works being done in high-resource languages like English. To advance Arabic NLP in the financial domain, the organizer of AraFinNLP 2024 has arranged a shared task for detecting banking intents from the queries in various Arabic dialects, introducing a novel dataset named ArBanking77 which includes a collection of banking queries categorized into 77 distinct intents classes. To accomplish this task, we have presented a hierarchical approach called Dual-Phase-BERT in which the detection of dialects is carried out first, followed by the detection of banking intents. Using the provided ArBanking77 dataset, we have trained and evaluated several conventional machine learning, and deep learning models along with some cutting-edge transformer-based models. Among these models, our proposed Dual-Phase-BERT model has ranked 7^th out of all competitors, scoring 0.801 on the scale of F1-score on the test set.

In recent days, propaganda has started to influence public opinion increasingly as social media usage continues to grow. Our research has been part of the first challenge, Unimodal (Text) Propagandistic Technique Detection of ArAIEval shared task at the ArabicNLP 2024 conference, co-located with ACL 2024, identifying specific Arabic text spans using twenty-three propaganda techniques. We have augmented underrepresented techniques in the provided dataset using synonym replacement and have evaluated various machine learning (RF, SVM, MNB), deep learning (BiLSTM), and transformer-based models (bert-base-arabic, Marefa-NER, AraBERT) with transfer learning. Our comparative study has shown that the transformer model “bert-base-arabic” has outperformed other models. Evaluating the test set, it has achieved the micro-F1 score of 0.2995 which is the highest. This result has secured our team “CUET_sstm” first place among all participants in task 1 of the ArAIEval.

Predicting semantic textual relatedness (STR) is one of the most challenging tasks in the field of natural language processing. Semantic relatedness prediction has real-life practical applications while developing search engines and modern text generation systems. A shared task on semantic textual relatedness has been organized by SemEval 2024, where the organizer has proposed a dataset on semantic textual relatedness in the English language under Shared Task 1 (Track A3). In this work, we have developed models to predict semantic textual relatedness between pairs of English sentences by training and evaluating various transformer-based model architectures, deep learning, and machine learning methods using the shared dataset. Moreover, we have utilized existing semantic textual relatedness datasets such as the stsb multilingual benchmark dataset, the SemEval 2014 Task 1 dataset, and the SemEval 2015 Task 2 dataset. Our findings show that in the SemEval 2024 Shared Task 1 (Track A3), the fine-tuned-STS-BERT model performed the best, scoring 0.8103 on the test set and placing 25th out of all participants.

The availability of the internet has made it easier for people to share information via social media. People with ill intent can use this widespread availability of the internet to share violent content easily. A significant portion of social media users prefer using their regional language which makes it quite difficult to detect violence-inciting text. The objective of our research work is to detect Bangla violence-inciting text from social media content. A shared task on Bangla violence-inciting text detection has been organized by the First Bangla Language Processing Workshop (BLP) co-located with EMNLP, where the organizer has provided a dataset named VITD with three categories: nonviolence, passive violence, and direct violence text. To accomplish this task, we have implemented three machine learning models (RF, SVM, XGBoost), two deep learning models (LSTM, BiLSTM), and two transformer-based models (BanglaBERT, Hierarchical-BERT). We have conducted a comparative study among different models by training and evaluating each model on the VITD dataset. We have found that Hierarchical-BERT has provided the best result with an F1 score of 0.73797 on the test set and ranked 9th position among all participants in the shared task 1 of the BLP Workshop co-located with EMNLP 2023.

With the popularity of social media platforms, people are sharing their individual thoughts by posting, commenting, and messaging with their friends, which generates a significant amount of digital text data every day. Conducting sentiment analysis of social media content is a vibrant research domain within the realm of Natural Language Processing (NLP), and it has practical, real-world uses. Numerous prior studies have focused on sentiment analysis for languages that have abundant linguistic resources, such as English. However, limited prior research works have been done for automatic sentiment analysis in low-resource languages like Bangla. In this research work, we are going to finetune different transformer-based models for Bangla sentiment analysis. To train and evaluate the model, we have utilized a dataset provided in a shared task organized by the BLP Workshop co-located with EMNLP-2023. Moreover, we have conducted a comparative study among different machine learning models, deep learning models, and transformer-based models for Bangla sentiment analysis. Our findings show that the BanglaBERT (Large) model has achieved the best result with a micro F1-Score of 0.7109 and secured 7th position in the shared task 2 leaderboard of the BLP Workshop in EMNLP 2023.