ymoslem/acl-abstracts huggingface dataset:
Contextual Bangla Neural Stemmer: Finding Contextualized Root Word Representations for Bangla Words (2023)

Stemmers are commonly used in NLP to reduce words to their root form. However, this process may discard important information and yield incorrect root forms, affecting the accuracy of NLP tasks. To address these limitations, we propose a Contextual Bangla Neural Stemmer for Bangla language to enhance word representations. Our method involves splitting words into characters within the Neural Stemming Block, obtaining vector representations for both stem words and unknown vocabulary words. A loss function aligns these representations with Word2Vec representations, followed by contextual word representations from a Universal Transformer encoder. Mean Pooling generates sentence-level representations that are aligned with BanglaBERT`s representations using a MLP layer. The proposed model also tries to build good representations for out-of-vocabulary (OOV) words. Experiments with our model on five Bangla datasets shows around 5{\%} average improvement over the vanilla approach. Notably, our method avoids BERT retraining, focusing on root word detection and addressing OOV and sub-word issues. By incorporating our approach into a large corpus-based Language Model, we expect further improvements in aspects like explainability.
Investigating the Effectiveness of Graph-based Algorithm for Bangla Text Classification (2023)

In this study, we examine and analyze the behavior of several graph-based models for Bangla text classification tasks. Graph-based algorithms create heterogeneous graphs from text data. Each node represents either a word or a document, and each edge indicates relationship between any two words or word and document. We applied the BERT model and different graph-based models including TextGCN, GAT, BertGAT, and BertGCN on five different datasets including SentNoB, Sarcasm detection, BanFakeNews, Hate speech detection, and Emotion detection datasets for Bangla text. BERT`s model bested the TextGCN and the GAT models by a large difference in terms of accuracy, Macro F1 score, and weighted F1 score. BertGCN and BertGAT are shown to outperform standalone graph models and BERT model. BertGAT excelled in the Emotion detection dataset and achieved a 1{\%}-2{\%} performance boost in Sarcasm detection, Hate speech detection, and BanFakeNews datasets from BERT`s performance. Whereas, BertGCN outperformed BertGAT by 1{\%} for SetNoB, and BanFakeNews datasets while beating BertGAT by 2{\%} for Sarcasm detection, Hate Speech, and Emotion detection datasets. We also examined different variations in graph structure and analyzed their effects.
SynthNID: Synthetic Data to Improve End-to-end Bangla Document Key Information Extraction (2023)

End-to-end Document Key Information Extraction models require a lot of compute and labeled data to perform well on real datasets. This is particularly challenging for low-resource languages like Bangla where domain-specific multimodal document datasets are scarcely available. In this paper, we have introduced SynthNID, a system to generate domain-specific document image data for training OCR-less end-to-end Key Information Extraction systems. We show the generated data improves the performance of the extraction model on real datasets and the system is easily extendable to generate other types of scanned documents for a wide range of document understanding tasks. The code for generating synthetic data is available at https://github.com/dv66/synthnid
BaTEClaCor: A Novel Dataset for Bangla Text Error Classification and Correction (2023)

In the context of the dynamic realm of Bangla communication, online users are often prone to bending the language or making errors due to various factors. We attempt to detect, categorize, and correct those errors by employing several machine learning and deep learning models. To contribute to the preservation and authenticity of the Bangla language, we introduce a meticulously categorized organic dataset encompassing 10,000 authentic Bangla comments from a commonly used social media platform. Through rigorous comparative analysis of distinct models, our study highlights BanglaBERT`s superiority in error-category classification and underscores the effectiveness of BanglaT5 for text correction. BanglaBERT achieves accuracy of 79.1{\%} and 74.1{\%} for binary and multiclass error-category classification while the BanglaBERT is fine-tuned and tested with our proposed dataset. Moreover, BanglaT5 achieves the best Rouge-L score (0.8459) when BanglaT5 is fine-tuned and tested with our corrected ground truths. Beyond algorithmic exploration, this endeavor represents a significant stride in enhancing the quality of digital discourse in the Bangla-speaking community, fostering linguistic precision and coherence in online interactions. The dataset and code is available at https://github.com/SyedT1/BaTEClaCor.
Crosslingual Retrieval Augmented In-context Learning for Bangla (2023)

The promise of Large Language Models (LLMs) in Natural Language Processing has often been overshadowed by their limited performance in low-resource languages such as Bangla. To address this, our paper presents a pioneering approach that utilizes cross-lingual retrieval augmented in-context learning. By strategically sourcing semantically similar prompts from high-resource language, we enable multilingual pretrained language models (MPLMs), especially the generative model BLOOMZ, to successfully boost performance on Bangla tasks. Our extensive evaluation highlights that the cross-lingual retrieval augmented prompts bring steady improvements to MPLMs over the zero-shot performance.
Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition (2023)

One of the major challenges for developing automatic speech recognition (ASR) for low-resource languages is the limited access to labeled data with domain-specific variations. In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. With the proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We then exploited the developed corpus to design a conformer-based ASR system. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. To investigate the efficacy, we designed and developed a human-annotated domain-agnostic test set composed of news, telephony, and conversational data among others. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets. The experimental resources will be publicly available.https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR
BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bangla (2023)

This paper presents the system that we have developed while solving this shared task on violence inciting text detection in Bangla. We explain both the traditional and the recent approaches that we have used to make our models learn. Our proposed system helps to classify if the given text contains any threat. We studied the impact of data augmentation when there is a limited dataset available. Our quantitative results show that finetuning a multilingual-e5-base model performed the best in our task compared to other transformer-based architectures. We obtained a macro F1 of 68.11{\%} in the test set and our performance in this shared task is ranked at 23 in the leaderboard.
Team CentreBack at BLP-2023 Task 1: Analyzing performance of different machine-learning based methods for detecting violence-inciting texts in Bangla (2023)

Like all other things in the world, rapid growth of social media comes with its own merits and demerits. While it is providing a platform for the world to easily communicate with each other, on the other hand the room it has opened for hate speech has led to a significant impact on the well-being of the users. These types of texts have the potential to result in violence as people with similar sentiments may be inspired to commit violent acts after coming across such comments. Hence, the need for a system to detect and filter such texts is increasing drastically with time. This paper summarizes our experimental results and findings for the shared task on The First Bangla Language Processing Workshop at EMNLP 2023 - Singapore. We participated in the shared task 1 : Violence Inciting Text Detection (VITD). The objective was to build a system that classifies the given comments as either non-violence, passive violence or direct violence. We tried out different techniques, such as fine-tuning language models, few-shot learning with SBERT and a 2 stage training where we performed binary violence/non-violence classification first, then did a fine-grained classification of direct/passive violence. We found that the best macro-F1 score of 69.39 was yielded by fine-tuning the BanglaBERT language model and we attained a position of 21 among 27 teams in the final leaderboard. After the competition ended, we found that with some preprocessing of the dataset, we can get the score up to 71.68.
EmptyMind at BLP-2023 Task 1: A Transformer-based Hierarchical-BERT Model for Bangla Violence-Inciting Text Detection (2023)

The availability of the internet has made it easier for people to share information via social media. People with ill intent can use this widespread availability of the internet to share violent content easily. A significant portion of social media users prefer using their regional language which makes it quite difficult to detect violence-inciting text. The objective of our research work is to detect Bangla violence-inciting text from social media content. A shared task on Bangla violence-inciting text detection has been organized by the First Bangla Language Processing Workshop (BLP) co-located with EMNLP, where the organizer has provided a dataset named VITD with three categories: nonviolence, passive violence, and direct violence text. To accomplish this task, we have implemented three machine learning models (RF, SVM, XGBoost), two deep learning models (LSTM, BiLSTM), and two transformer-based models (BanglaBERT, Hierarchical-BERT). We have conducted a comparative study among different models by training and evaluating each model on the VITD dataset. We have found that Hierarchical-BERT has provided the best result with an F1 score of 0.73797 on the test set and ranked 9th position among all participants in the shared task 1 of the BLP Workshop co-located with EMNLP 2023.
nlpBDpatriots at BLP-2023 Task 1: Two-Step Classification for Violence Inciting Text Detection in Bangla - Leveraging Back-Translation and Multilinguality (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 $6^{th}$ out of 27 teams with a macro F1 score of 0.74.
Score\_IsAll\_You\_Need at BLP-2023 Task 1: A Hierarchical Classification Approach to Detect Violence Inciting Text using Transformers (2023)

Violence-inciting text detection has become critical due to its significance in social media monitoring, online security, and the prevention of violent content. Developing an automatic text classification model for identifying violence in languages with limited resources, like Bangla, poses significant challenges due to the scarcity of resources and complex morphological structures. This work presents a transformer-based method that can classify Bangla texts into three violence classes: direct, passive, and non-violence. We leveraged transformer models, including BanglaBERT, XLM-R, and m-BERT, to develop a hierarchical classification model for the downstream task. In the first step, the BanglaBERT is employed to identify the presence of violence in the text. In the next step, the model classifies stem texts that incite violence as either direct or passive. The developed system scored 72.37 and ranked 14th among the participants.
Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language Models for Violence Inciting Text Detection (2023)

This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing. Social media has accelerated the propagation of hate and violence-inciting speech in society. It is essential to develop efficient mechanisms to detect and curb the propagation of such texts. The problem of detecting violence-inciting texts is further exacerbated in low-resource settings due to sparse research and less data. The data provided in the shared task consists of texts in the Bangla language, where each example is classified into one of the three categories defined based on the types of violence-inciting texts. We try and evaluate several BERT-based models, and then use an ensemble of the models as our final submission. Our submission is ranked 10th in the final leaderboard of the shared task with a macro F1 score of 0.737.
VacLM at BLP-2023 Task 1: Leveraging BERT models for Violence detection in Bangla (2023)

This study introduces the system submitted to the BLP Shared Task 1: Violence Inciting Text Detection (VITD) by the VacLM team. In this work, we analyzed the impact of various transformer-based models for detecting violence in texts. BanglaBERT outperforms all the other competing models. We also observed that the transformer-based models are not adept at classifying Passive Violence and Direct Violence class but can better detect violence in texts, which was the task`s primary objective. On the shared task, we secured a rank of 12 with macro F1-score of 72.656{\%}.
Aambela at BLP-2023 Task 1: Focus on UNK tokens: Analyzing Violence Inciting Bangla Text with Adding Dataset Specific New Word Tokens (2023)

The BLP-2023 Task 1 aims to develop a Natural Language Inference system tailored for detecting and analyzing threats from Bangla YouTube comments. Bangla language models like BanglaBERT have demonstrated remarkable performance in various Bangla natural language processing tasks across different domains. We utilized BanglaBERT for the violence detection task, employing three different classification heads. As BanglaBERT`s vocabulary lacks certain crucial words, our model incorporates some of them as new special tokens, based on their frequency in the dataset, and their embeddings are learned during training. The model achieved the 2nd position on the leaderboard, boasting an impressive macro-F1 Score of 76.04{\%} on the official test set. With the addition of new tokens, we achieved a 76.90{\%} macro-F1 score, surpassing the top score (76.044{\%}) on the test set.
SUST\_Black Box at BLP-2023 Task 1: Detecting Communal Violence in Texts: An Exploration of MLM and Weighted Ensemble Techniques (2023)

In this study, we address the shared task of classifying violence-inciting texts from YouTube comments related to violent incidents in the Bengal region. We seamlessly integrated domain adaptation techniques by meticulously fine-tuning pre-existing Masked Language Models on a diverse array of informal texts. We employed a multifaceted approach, leveraging Transfer Learning, Stacking, and Ensemble techniques to enhance our model`s performance. Our integrated system, amalgamating the refined BanglaBERT model through MLM and our Weighted Ensemble approach, showcased superior efficacy, achieving macro F1 scores of 71{\%} and 72{\%}, respectively, while the MLM approach secured the 18th position among participants. This underscores the robustness and precision of our proposed paradigm in the nuanced detection and categorization of violent narratives within digital realms.
the\_linguists at BLP-2023 Task 1: A Novel Informal Bangla Fasttext Embedding for Violence Inciting Text Detection (2023)

This paper introduces a novel informal Bangla word embedding for designing a cost-efficient solution for the task {\textquotedblleft}Violence Inciting Text Detection{\textquotedblright} 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.
UFAL-ULD at BLP-2023 Task 1: Violence Detection in Bangla Text (2023)

In this paper, we present UFAL-ULD team`s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task.
Semantics Squad at BLP-2023 Task 1: Violence Inciting Bangla Text Detection with Fine-Tuned Transformer-Based Models (2023)

This study investigates the application of Transformer-based models for violence threat identification. We participated in the BLP-2023 Shared Task 1 and in our initial submission, BanglaBERT large achieved 5th position on the leader-board with a macro F1 score of 0.7441, approaching the highest baseline of 0.7879 established for this task. In contrast, the top-performing system on the leaderboard achieved an F1 score of 0.7604. Subsequent experiments involving m-BERT, XLM-RoBERTa base, XLM-RoBERTa large, BanglishBERT, BanglaBERT, and BanglaBERT large models revealed that BanglaBERT achieved an F1 score of 0.7441, which closely approximated the baseline. Remarkably, m-BERT and XLM-RoBERTa base also approximated the baseline with macro F1 scores of 0.6584 and 0.6968, respectively. A notable finding from our study is the under-performance by larger models for the shared task dataset, which requires further investigation. Our findings underscore the potential of transformer-based models in identifying violence threats, offering valuable insights to enhance safety measures on online platforms.
LowResourceNLU at BLP-2023 Task 1 \& 2: Enhancing Sentiment Classification and Violence Incitement Detection in Bangla Through Aggregated Language Models (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.
Team Error Point at BLP-2023 Task 1: A Comprehensive Approach for Violence Inciting Text Detection using Deep Learning and Traditional Machine Learning Algorithm (2023)

In the modern digital landscape, social media platforms have the dual role of fostering unprecedented connectivity and harboring a dark underbelly in the form of widespread violence-inciting content. Pioneering research in Bengali social media aims to provide a groundbreaking solution to this issue. This study thoroughly investigates violence-inciting text classification using a diverse range of machine learning and deep learning models, offering insights into content moderation and strategies for enhancing online safety. Situated at the intersection of technology and social responsibility, the aim is to empower platforms and communities to combat online violence. By providing insights into model selection and methodology, this work makes a significant contribution to the ongoing dialogue about the challenges posed by the darker aspects of the digital era. Our system scored 31.913 and ranked 26 among the participants.
NLP\_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach (2023)

The amount of online textual content has increased significantly in recent years through social media posts, online chatting, web portals, and other digital platforms due to the significant increase in internet users and their unprompted access via digital devices. Unfortunately, the misappropriation of textual communication via the Internet has led to violence-inciting texts. Despite the availability of various forms of violence-inciting materials, text-based content is often used to carry out violent acts. Thus, developing a system to detect violence-inciting text has become vital. However, creating such a system in a low-resourced language like Bangla becomes challenging. Therefore, a shared task has been arranged to detect violence-inciting text in Bangla. This paper presents a hybrid approach (GAN+Bangla-ELECTRA) to classify violence-inciting text in Bangla into three classes: \textit{direct}, \textit{passive}, and \textit{non-violence}. We investigated a variety of deep learning (CNN, BiLSTM, BiLSTM+Attention), machine learning (LR, DT, MNB, SVM, RF, SGD), transformers (BERT, ELECTRA), and GAN-based models to detect violence inciting text in Bangla. Evaluation results demonstrate that the GAN+Bangla-ELECTRA model gained the highest macro $f_1$-score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.
Team\_Syrax at BLP-2023 Task 1: Data Augmentation and Ensemble Based Approach for Violence Inciting Text Detection in Bangla (2023)

This paper describes our participation in Task1 (VITD) of BLP Workshop 1 at EMNLP 2023,focused on the detection and categorizationof threats linked to violence, which could po-tentially encourage more violent actions. Ourapproach involves fine-tuning of pre-trainedtransformer models and employing techniqueslike self-training with external data, data aug-mentation through back-translation, and en-semble learning (bagging and majority voting).Notably, self-training improves performancewhen applied to data from external source butnot when applied to the test-set. Our anal-ysis highlights the effectiveness of ensemblemethods and data augmentation techniques inBangla Text Classification. Our system ini-tially scored 0.70450 and ranked 19th amongthe participants but post-competition experi-ments boosted our score to 0.72740.