2023
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KnowTellConvince at ArAIEval Shared Task: Disinformation and Persuasion Detection in Arabic using Similar and Contrastive Representation Alignment
Hariram Veeramani
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Surendrabikram Thapa
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Usman Naseem
Proceedings of ArabicNLP 2023
In an era of widespread digital communication, the challenge of identifying and countering disinformation has become increasingly critical. However, compared to the solutions available in the English language, the resources and strategies for tackling this multifaceted problem in Arabic are relatively scarce. To address this issue, this paper presents our solutions to tasks in ArAIEval 2023. Task 1 focuses on detecting persuasion techniques, while Task 2 centers on disinformation detection within Arabic text. Leveraging a multi-head model architecture, fine-tuning techniques, sequential learning, and innovative activation functions, our contributions significantly enhance persuasion techniques and disinformation detection accuracy. Beyond improving performance, our work fills a critical research gap in content analysis for Arabic, empowering individuals, communities, and digital platforms to combat deceptive content effectively and preserve the credibility of information sources within the Arabic-speaking world.
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DialectNLU at NADI 2023 Shared Task: Transformer Based Multitask Approach Jointly Integrating Dialect and Machine Translation Tasks in Arabic
Hariram Veeramani
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Surendrabikram Thapa
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Usman Naseem
Proceedings of ArabicNLP 2023
With approximately 400 million speakers worldwide, Arabic ranks as the fifth most-spoken language globally, necessitating advancements in natural language processing. This paper addresses this need by presenting a system description of the approaches employed for the subtasks outlined in the Nuanced Arabic Dialect Identification (NADI) task at EMNLP 2023. For the first subtask, involving closed country-level dialect identification classification, we employ an ensemble of two Arabic language models. Similarly, for the second subtask, focused on closed dialect to Modern Standard Arabic (MSA) machine translation, our approach combines sequence-to-sequence models, all trained on an Arabic-specific dataset. Our team ranks 10th and 3rd on subtask 1 and subtask 2 respectively.
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LowResContextQA at Qur’an QA 2023 Shared Task: Temporal and Sequential Representation Augmented Question Answering Span Detection in Arabic
Hariram Veeramani
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Surendrabikram Thapa
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Usman Naseem
Proceedings of ArabicNLP 2023
The Qur’an holds immense theological and historical significance, and developing a technology-driven solution for answering questions from this sacred text is of paramount importance. This paper presents our approach to task B of Qur’an QA 2023, part of EMNLP 2023, addressing this challenge by proposing a robust method for extracting answers from Qur’anic passages. Leveraging the Qur’anic Reading Comprehension Dataset (QRCD) v1.2, we employ innovative techniques and advanced models to improve the precision and contextuality of answers derived from Qur’anic passages. Our methodology encompasses the utilization of start and end logits, Long Short-Term Memory (LSTM) networks, and fusion mechanisms, contributing to the ongoing dialogue at the intersection of technology and spirituality.
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LowResourceNLU at BLP-2023 Task 1 & 2: Enhancing Sentiment Classification and Violence Incitement Detection in Bangla Through Aggregated Language Models
Hariram Veeramani
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Surendrabikram Thapa
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Usman Naseem
Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
Violence incitement detection and sentiment analysis hold significant importance in the field of natural language processing. However, in the case of the Bangla language, there are unique challenges due to its low-resource nature. In this paper, we address these challenges by presenting an innovative approach that leverages aggregated BERT models for two tasks at the BLP workshop in EMNLP 2023, specifically tailored for Bangla. Task 1 focuses on violence-inciting text detection, while task 2 centers on sentiment analysis. Our approach combines fine-tuning with textual entailment (utilizing BanglaBERT), Masked Language Model (MLM) training (making use of BanglaBERT), and the use of standalone Multilingual BERT. This comprehensive framework significantly enhances the accuracy of sentiment classification and violence incitement detection in Bangla text. Our method achieved the 11th rank in task 1 with an F1-score of 73.47 and the 4th rank in task 2 with an F1-score of 71.73. This paper provides a detailed system description along with an analysis of the impact of each component of our framework.
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Semantists at ImageArg-2023: Exploring Cross-modal Contrastive and Ensemble Models for Multimodal Stance and Persuasiveness Classification
Kanagasabai Rajaraman
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Hariram Veeramani
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Saravanan Rajamanickam
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Adam Maciej Westerski
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Jung-Jae Kim
Proceedings of the 10th Workshop on Argument Mining
In this paper, we describe our system for ImageArg-2023 Shared Task that aims to identify an image’s stance towards a tweet and determine its persuasiveness score concerning a specific topic. In particular, the Shared Task proposes two subtasks viz. subtask (A) Multimodal Argument Stance (AS) Classification, and subtask (B) Multimodal Image Persuasiveness (IP) Classification, using a dataset composed of tweets (images and text) from controversial topics, namely gun control and abortion. For subtask A, we employ multiple transformer models using a text based approach to classify the argumentative stance of the tweet. For sub task B we adopted text based as well as multimodal learning methods to classify image persuasiveness of the tweet. Surprisingly, the text-based approach of the tweet overall performed better than the multimodal approaches considered. In summary, our best system achieved a F1 score of 0.85 for sub task (A) and 0.50 for subtask (B), and ranked 2nd in subtask (A) and 4th in subtask (B), among all teams submissions.