the_linguists at BLP -2023 Task 1: A Novel Informal B angla F asttext Embedding for Violence Inciting Text Detection (2023)

Abstract: This paper introduces a novel informal Bangla word embedding for designing a cost-efficient solution for the task “Violence Inciting Text Detection” which focuses on developing classification systems to categorize violence that can potentially incite further violent actions. We propose a semi-supervised learning approach by training an informal Bangla FastText embedding, which is further fine-tuned on lightweight models on task specific dataset and yielded competitive results to our initial method using BanglaBERT, which secured the 7th position with an f1-score of 73.98%. We conduct extensive experiments to assess the efficiency of the proposed embedding and how well it generalizes in terms of violence classification, along with it’s coverage on the task’s dataset. Our proposed Bangla IFT embedding achieved a competitive macro average F1 score of 70.45%. Additionally, we provide a detailed analysis of our findings, delving into potential causes of misclassification in the detection of violence-inciting text.

## introduction this study details our methods and results for the "violence inciting text detection (vitd)" task @xcite , aiming to classify texts into three violence categories: direct violence, passive violence, and non-violence with a goal to identify texts that could lead to further violent actions. unlike hate speech that targets groups based on attributes, violence-inciting texts advocate harm. the misuse of social media, especially in the bengal region, has escalated communal violence @xcite , with hate speech being a primary cause. this task aims to understand and mitigate such violence. our study introduces a unique bangla fast-text(ift) embedding trained on 3.8 million informal bangla text samples collected from informal data sources such as facebook and youtube comments. we combine this with lightweight ml and dl models like logistic regression (lr), svm, lstm, bilstm, and gru to detect violence-inciting texts and compare the performance with transformer models such as banglabert, mbert, xlm-roberta. to the best of our knowledge, this is the first attempt to use fasttext embeddings with lightweight models for detecting violence inciting texts in bangla. such methods have shown potential in various bangla text classification methods in previous studies @xcite . our contributions can be summarized as follows: the primary objective of this task is to detect and categorize threats associated with violence, which 1 https://github.com/tariquzzaman-faisal/ vitd have the potential to incite further acts of violence. the task features three distinct categories: ## system description the system proposed for the vitd shared task is based on ift embedding that incorporates sub-word information, enabling effective handling of out-of-vocabulary(oov) words and capturing morphological patterns. we follow a semisupervised methodology for training where the ift embedding is created by our collected unlabelled data from social media comments. this embedding is then finetuned on the task specific vitd dataset @xcite and incorporated with lightweight models like logistic regression (lr), svm, lstm, bilstm, and gru models. we carried out extensive experiments to validate the effectiveness of our method and utility of our proposed embedding. our proposed system is illustrated in figure 1 . the configuration used for lstm, bil-stm, and gru models are included in the ## conclusion this paper presents a cost-sensitive approach to the detection of violence-inciting text in bangla using a semi-supervised method. our results show that applying the proposed ift embedding to lightweight models produces competitive performance compared to larger transformer models, all while maintaining cost-effectiveness. we believe that enhancing the dataset's size and coverage will lead to improved performance across various aspects when using ift, thereby broadening the potential applications of our approach to other bangla text classification tasks.