2024
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MasonTigers@LT-EDI-2024: An Ensemble Approach Towards Detecting Homophobia and Transphobia in Social Media Comments
Dhiman Goswami
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Sadiya Sayara Chowdhury Puspo
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Md Nishat Raihan
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Al Nahian Bin Emran
Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
In this paper, we describe our approaches and results for Task 2 of the LT-EDI 2024 Workshop, aimed at detecting homophobia and/or transphobia across ten languages. Our methodologies include monolingual transformers and ensemble methods, capitalizing on the strengths of each to enhance the performance of the models. The ensemble models worked well, placing our team, MasonTigers, in the top five for eight of the ten languages, as measured by the macro F1 score. Our work emphasizes the efficacy of ensemble methods in multilingual scenarios, addressing the complexities of language-specific tasks.
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MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles
Amrita Ganguly
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Al Nahian Bin Emran
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Sadiya Sayara Chowdhury Puspo
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Md Nishat Raihan
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Dhiman Goswami
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Marcos Zampieri
Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.
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MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification
Al Nahian Bin Emran
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Amrita Ganguly
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Sadiya Sayara Chowdhury Puspo
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Dhiman Goswami
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Md Nishat Raihan
Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
The task of identifying public opinions on social media, particularly regarding climate activism and the detection of hate events, has emerged as a critical area of research in our rapidly changing world. With a growing number of people voicing either to support or oppose to climate-related issues - understanding these diverse viewpoints has become increasingly vital. Our team, MasonPerplexity, participates in a significant research initiative focused on this subject. We extensively test various models and methods, discovering that our most effective results are achieved through ensemble modeling, enhanced by data augmentation techniques like back-translation. In the specific components of this research task, our team achieved notable positions, ranking 5th, 1st, and 6th in the respective sub-tasks, thereby illustrating the effectiveness of our approach in this important field of study.
2023
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Offensive Language Identification in Transliterated and Code-Mixed Bangla
Md Nishat Raihan
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Umma Tanmoy
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Anika Binte Islam
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Kai North
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Tharindu Ranasinghe
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Antonios Anastasopoulos
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Marcos Zampieri
Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
Identifying offensive content in social media is vital to create safe online communities. Several recent studies have addressed this problem by creating datasets for various languages. In this paper, we explore offensive language identification in texts with transliterations and code-mixing, linguistic phenomena common in multilingual societies, and a known challenge for NLP systems. We introduce TB-OLID, a transliterated Bangla offensive language dataset containing 5,000 manually annotated comments. We train and fine-tune machine learning models on TB-OLID, and we evaluate their results on this dataset. Our results show that English pre-trained transformer-based models, such as fBERT and HateBERT achieve the best performance on this dataset.
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nlpBDpatriots at BLP-2023 Task 1: Two-Step Classification for Violence Inciting Text Detection in Bangla - Leveraging Back-Translation and Multilinguality
Md Nishat Raihan
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Dhiman Goswami
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Sadiya Sayara Chowdhury Puspo
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Marcos Zampieri
Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
In this paper, we discuss the nlpBDpatriots entry to the shared task on Violence Inciting Text Detection (VITD) organized as part of the first workshop on Bangla Language Processing (BLP) co-located with EMNLP. The aim of this task is to identify and classify the violent threats, that provoke further unlawful violent acts. Our best-performing approach for the task is two-step classification using back translation and multilinguality which ranked 6th out of 27 teams with a macro F1 score of 0.74.
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nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis
Dhiman Goswami
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Md Nishat Raihan
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Sadiya Sayara Chowdhury Puspo
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Marcos Zampieri
Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
In this paper, we discuss the entry of nlpBDpatriots to some sophisticated approaches for classifying Bangla Sentiment Analysis. This is a shared task of the first workshop on Bangla Language Processing (BLP) organized under EMNLP. The main objective of this task is to identify the sentiment polarity of social media content. There are 30 groups of NLP enthusiasts who participate in this shared task and our best-performing approach for the task is transfer learning with data augmentation. Our group ranked 12th position in this competition with this methodology securing a micro F1 score of 0.71.
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SentMix-3L: A Novel Code-Mixed Test Dataset in Bangla-English-Hindi for Sentiment Analysis
Md Nishat Raihan
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Dhiman Goswami
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Antara Mahmud
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Antonios Anastasopoulos
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Marcos Zampieri
Proceedings of the First Workshop in South East Asian Language Processing
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OffMix-3L: A Novel Code-Mixed Test Dataset in Bangla-English-Hindi for Offensive Language Identification
Dhiman Goswami
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Md Nishat Raihan
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Antara Mahmud
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Antonios Anastasopoulos
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Marcos Zampieri
Proceedings of the 11th International Workshop on Natural Language Processing for Social Media