The upsurge of social media and open source platforms has created new avenues for the rapid, global spread of negativity and obscenities targeting individuals and organizations. The process to identify hate speech is critical for the lexical and regional variation as well as the morphological complexity of the texts, especially in low-resource languages, e.g. Bangla. This paper presents our participation in the Hate Speech Detection task at the second workshop on Bangla Language Processing. The objective of this task is not only to detect whether the content is hateful, but also to identify the type of hate, the target group, and its severity. We proposed a Transformer-based weighted probabilistic fusion model to detect the presence of hate speech in Bangla texts. We independently fine-tuned three pre-trained Transformer models, BanglaBERT, XLM-RoBERTa, and MuRIL, to capture diverse linguistic representations. The probability distributions obtained from each model were combined using a weighted fusion strategy, allowing the system to leverage the strengths of all models simultaneously. This fused representation was then used to predict the final labels for the given instances. The experimental results showed that our proposed method obtained competitive performance, ranking 10th in subtask 1A and 15th in subtask 1B among the participants.

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.

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.