Abdul Aziz


2023

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CSECU-DSG at SemEval-2023 Task 4: Fine-tuning DeBERTa Transformer Model with Cross-fold Training and Multi-sample Dropout for Human Values Identification
Abdul Aziz | Md. Akram Hossain | Abu Nowshed Chy
Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)

Human values identification from a set of argument is becoming a prominent area of research in argument mining. Among some options, values convey what may be the most desirable and widely accepted answer. The diversity of human beliefs, random texture and implicit meaning within the arguments makes it more difficult to identify human values from the arguments. To address these challenges, SemEval-2023 Task 4 introduced a shared task ValueEval focusing on identifying human values categories based on given arguments. This paper presents our participation in this task where we propose a finetuned DeBERTa transformers-based classification approach to identify the desire human value category. We utilize different training strategy with the finetuned DeBERTa model to enhance contextual representation on this downstream task. Our proposed method achieved competitive performance among the participants’ methods.

2022

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CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer Models
Abdul Aziz | Md. Akram Hossain | Abu Nowshed Chy
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Recognizing lexical relationships between words is one of the formidable tasks in computational linguistics. It plays a vital role in the improvement of various NLP tasks. However, the diversity of word semantics, sentence structure as well as word order information make it challenging to distill the relationship effectively. To address these challenges, SemEval-2022 Task 3 introduced a shared task PreTENS focusing on semantic competence to determine the taxonomic relations between two nominal arguments. This paper presents our participation in this task where we proposed an approach through exploiting an ensemble of multilingual transformer methods. We employed two fine-tuned multilingual transformer models including XLM-RoBERTa and mBERT to train our model. To enhance the performance of individual models, we fuse the predicted probability score of these two models using weighted arithmetic mean to generate a unified probability score. The experimental results showed that our proposed method achieved competitive performance among the participants’ methods.

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CSECU-DSG at SemEval-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based Approach
Abdul Aziz | Md. Akram Hossain | Abu Nowshed Chy
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Recognizing complex and ambiguous named entities (NEs) is one of the formidable tasks in the NLP domain. However, the diversity of linguistic constituents, syntactic structure, semantic ambiguity as well as differences from traditional NEs make it challenging to identify the complex NEs. To address these challenges, SemEval-2022 Task 11 introduced a shared task MultiCoNER focusing on complex named entity recognition in multilingual settings. This paper presents our participation in this task where we propose two different approaches including a BiLSTM-CRF model with stacked-embedding strategy and a transformer-based approach. Our proposed method achieved competitive performance among the participants’ methods in a few languages.

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Enhancing the DeBERTa Transformers Model for Classifying Sentences from Biomedical Abstracts
Abdul Aziz | Md. Akram Hossain | Abu Nowshed Chy
Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association

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CSECU-DSG @ Causal News Corpus 2022: Fusion of RoBERTa Transformers Variants for Causal Event Classification
Abdul Aziz | Md. Akram Hossain | Abu Nowshed Chy
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)

Identifying cause-effect relationships in sentences is one of the formidable tasks to tackle the challenges of inference and understanding of natural language. However, the diversity of word semantics and sentence structure makes it challenging to determine the causal relationship effectively. To address these challenges, CASE-2022 shared task 3 introduced a task focusing on event causality identification with causal news corpus. This paper presents our participation in this task, especially in subtask 1 which is the causal event classification task. To tackle the task challenge, we propose a unified neural model through exploiting two fine-tuned transformer models including RoBERTa and Twitter-RoBERTa. For the score fusion, we combine the prediction scores of each component model using weighted arithmetic mean to generate the probability score for class label identification. The experimental results showed that our proposed method achieved the top performance (ranked 1st) among the participants.

2021

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CSECU-DSG at SemEval-2021 Task 1: Fusion of Transformer Models for Lexical Complexity Prediction
Abdul Aziz | MD. Akram Hossain | Abu Nowshed Chy
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Lexical complexity prediction (LCP) conveys the anticipation of the complexity level of a token or a set of tokens in a sentence. It plays a vital role in the improvement of various NLP tasks including lexical simplification, translations, and text generation. However, multiple meaning of a word in multiple circumstances, grammatical complex structure, and the mutual dependency of words in a sentence make it difficult to estimate the lexical complexity. To address these challenges, SemEval-2021 Task 1 introduced a shared task focusing on LCP and this paper presents our participation in this task. We proposed a transformer-based approach with sentence pair regression. We employed two fine-tuned transformer models. Including BERT and RoBERTa to train our model and fuse their predicted score to the complexity estimation. Experimental results demonstrate that our proposed method achieved competitive performance compared to the participants’ systems.