ymoslem/acl-abstracts huggingface dataset:
BLP-2023 Task 1: Violence Inciting Text Detection (VITD) (2023)

We present the comprehensive technical description of the outcome of the BLP shared task on Violence Inciting Text Detection (VITD).In recent years, social media has become a tool for groups of various religions and backgrounds to spread hatred, leading to physicalviolence with devastating consequences. To address this challenge, the VITD shared task was initiated, aiming to classify the level of violence incitement in various texts. The competition garnered significant interest with a total of 27 teams consisting of 88 participants successfully submitting their systems to the CodaLab leaderboard. During the post-workshop phase, we received 16 system papers on VITD from those participants. In this paper, we intend to discuss the VITD baseline performance, error analysis of the submitted models, and provide a comprehensive summary of the computational techniques applied by the participating teams
BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media Posts (2023)

Bangla is the 7th most widely spoken language globally, with a staggering 234 million native speakers primarily hailing from India and Bangladesh. This morphologically rich language boasts a rich literary tradition, encompassing diverse dialects and language-specific challenges. Despite its linguistic richness and history, Bangla remains categorized as a low-resource language within the natural language processing (NLP) and speech community. This paper presents our submission to Task 2 (Sentiment Analysis of Bangla Social Media Posts) of the BLP Workshop. We experimented with various Transformer-based architectures to solve this task. Our quantitative results show that transfer learning really helps in better learning of the models in this low-resource language scenario. This becomes evident when we further finetuned a model that had already been finetuned on Twitter data for sentiment analysis task and that finetuned model performed the best among all other models. We also performed a detailed error analysis where we found some instances where ground truth labels need to be looked at. We obtained a micro-F1 of 67.02{\%} on the test set and our performance in this shared task is ranked at 21 in the leaderboard.
Knowdee at BLP-2023 Task 2: Improving Bangla Sentiment Analysis Using Ensembled Models with Pseudo-Labeling (2023)

This paper outlines our submission to the Sentiment Analysis Shared Task at the Bangla Language Processing (BLP) Workshop at EMNLP2023 (Hasan et al., 2023a). The objective of this task is to detect sentiment in each text by classifying it as Positive, Negative, or Neutral. This shared task is based on the MUltiplatform BAngla SEntiment (MUBASE) (Hasan et al., 2023b) and SentNob (Islam et al., 2021) dataset, which consists of public comments from various social media platforms. Our proposed method for this task is based on the pre-trained Bangla language model BanglaBERT (Bhattacharjee et al., 2022). We trained an ensemble of BanglaBERT on the original dataset and used it to generate pseudo-labels for data augmentation. This expanded dataset was then used to train our final models. During the evaluation phase, 30 teams submitted their systems, and our system achieved the second highest performance with F1 score of 0.7267. The source code of the proposed approach is available at https://github.com/KnowdeeAI/blp{\_}task2{\_}knowdee.git.
M1437 at BLP-2023 Task 2: Harnessing Bangla Text for Sentiment Analysis: A Transformer-based Approach (2023)

Analyzing public sentiment on social media is helpful in understanding the public`s emotions about any given topic. While numerous studies have been conducted in this field, there has been limited research on Bangla social media data. Team M1437 from George Mason University participated in the Sentiment Analysis shared task of the Bangla Language Processing (BLP) Workshop at EMNLP-2023. The team fine-tuned various BERT-based Transformer architectures to solve the task. This article shows that $BanglaBERT_{large}$, a language model pre-trained on Bangla text, outperformed other BERT-based models. This model achieved an F1 score of 73.15{\%} and top position in the development phase, was further tuned with external training data, and achieved an F1 score of 70.36{\%} in the evaluation phase, securing the fourteenth place on the leaderboard. The F1 score on the test set, when $BanglaBERT_{large}$ was trained without external training data, was 71.54{\%}.
nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach towards Bangla Sentiment Analysis (2023)

In this paper, we discuss the entry of nlpBDpatriots to some sophisticated approaches for classifying Bangla Sentiment Analysis. This is a shared task of the first workshop on Bangla Language Processing (BLP) organized under EMNLP. The main objective of this task is to identify the sentiment polarity of social media content. There are 30 groups of NLP enthusiasts who participate in this shared task and our best-performing approach for the task is transfer learning with data augmentation. Our group ranked $12^{th}$ position in this competition with this methodology securing a micro F1 score of 0.71.
Ushoshi2023 at BLP-2023 Task 2: A Comparison of Traditional to Advanced Linguistic Models to Analyze Sentiment in Bangla Texts (2023)

This article describes our analytical approach designed for BLP Workshop-2023 Task-2: in Sentiment Analysis. During actual task submission, we used DistilBERT. However, we later applied rigorous hyperparameter tuning and pre-processing, improving the result to 68{\%} accuracy and a 68{\%} F1 micro score with vanilla LSTM. Traditional machine learning models were applied to compare the result where 75{\%} accuracy was achieved with traditional SVM. Our contributions are a) data augmentation using the oversampling method to remove data imbalance and b) attention masking for data encoding with masked language modeling to capture representations of language semantics effectively, by further demonstrating it with explainable AI. Originally, our system scored 0.26 micro-F1 in the competition and ranked 30th among the participants for a basic DistilBERT model, which we later improved to 0.68 and 0.65 with LSTM and XLM-RoBERTa-base models, respectively.
EmptyMind at BLP-2023 Task 2: Sentiment Analysis of Bangla Social Media Posts using Transformer-Based Models (2023)

With the popularity of social media platforms, people are sharing their individual thoughts by posting, commenting, and messaging with their friends, which generates a significant amount of digital text data every day. Conducting sentiment analysis of social media content is a vibrant research domain within the realm of Natural Language Processing (NLP), and it has practical, real-world uses. Numerous prior studies have focused on sentiment analysis for languages that have abundant linguistic resources, such as English. However, limited prior research works have been done for automatic sentiment analysis in low-resource languages like Bangla. In this research work, we are going to finetune different transformer-based models for Bangla sentiment analysis. To train and evaluate the model, we have utilized a dataset provided in a shared task organized by the BLP Workshop co-located with EMNLP-2023. Moreover, we have conducted a comparative study among different machine learning models, deep learning models, and transformer-based models for Bangla sentiment analysis. Our findings show that the BanglaBERT (Large) model has achieved the best result with a micro F1-Score of 0.7109 and secured 7th position in the shared task 2 leaderboard of the BLP Workshop in EMNLP 2023.
RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers (2023)

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world`s sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://github.com/ptnv-s/RSM-NLP-BLP-Task2 .
Semantics Squad at BLP-2023 Task 2: Sentiment Analysis of Bangla Text with Fine Tuned Transformer Based Models (2023)

Sentiment analysis (SA) is a crucial task in natural language processing, especially in contexts with a variety of linguistic features, like Bangla. We participated in BLP-2023 Shared Task 2 on SA of Bangla text. We investigated the performance of six transformer-based models for SA in Bangla on the shared task dataset. We fine-tuned these models and conducted a comprehensive performance evaluation. We ranked 20th on the leaderboard of the shared task with a blind submission that used BanglaBERT Small. BanglaBERT outperformed other models with 71.33{\%} accuracy, and the closest model was BanglaBERT Large, with an accuracy of 70.90{\%}. BanglaBERT consistently outperformed others, demonstrating the benefits of models developed using sizable datasets in Bangla.
Aambela at BLP-2023 Task 2: Enhancing BanglaBERT Performance for Bangla Sentiment Analysis Task with In Task Pretraining and Adversarial Weight Perturbation (2023)

This paper introduces the top-performing approachof {\textquotedblleft}Aambela{\textquotedblright} for the BLP-2023 Task2: {\textquotedblleft}Sentiment Analysis of Bangla Social MediaPosts{\textquotedblright}. The objective of the task was tocreate systems capable of automatically detectingsentiment in Bangla text from diverse socialmedia posts. My approach comprised finetuninga Bangla Language Model with threedistinct classification heads. To enhance performance,we employed two robust text classificationtechniques. To arrive at a final prediction,we employed a mode-based ensemble approachof various predictions from different models,which ultimately resulted in the 1st place in thecompetition.
Z-Index at BLP-2023 Task 2: A Comparative Study on Sentiment Analysis (2023)

In this study, we report our participation in Task 2 of the BLP-2023 shared task. The main objective of this task is to determine the sentiment (Positive, Neutral, or Negative) of a given text. We first removed the URLs, hashtags, and other noises and then applied traditional and pretrained language models. We submitted multiple systems in the leaderboard and BanglaBERT with tokenized data provided thebest result and we ranked 5th position in the competition with an F1-micro score of 71.64. Our study also reports that the importance of tokenization is lessening in the realm of pretrained language models. In further experiments, our evaluation shows that BanglaBERT outperforms, and predicting the neutral class is still challenging for all the models.
Team Error Point at BLP-2023 Task 2: A Comparative Exploration of Hybrid Deep Learning and Machine Learning Approach for Advanced Sentiment Analysis Techniques. (2023)

This paper presents a thorough and extensive investigation into the diverse models and techniques utilized for sentiment analysis. What sets this research apart is the deliberate and purposeful incorporation of data augmentation techniques with the goal of improving the efficacy of sentiment analysis in the Bengali language. We systematically explore various approaches, including preprocessing techniques, advancedmodels like Long Short-Term Memory (LSTM) and LSTM-CNN (Convolutional Neural Network) Combine, and traditional machine learning models such as Logistic Regression, Decision Tree, Random Forest, Multi-Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent. Our study highlights the substantial impact of data augmentation on enhancing model accuracy and understanding Bangla sentiment nuances. Additionally, we emphasize the LSTM model`s ability to capture long-range correlations in Bangla text. Our system scored 0.4129 and ranked 27th among the participants.
UFAL-ULD at BLP-2023 Task 2 Sentiment Classification in Bangla Text (2023)

In this paper, we present the UFAL-ULD team`s system for the BLP Shared Task 2: Sentiment Analysis of Bangla Social Media Posts. The Task 2 involves classifying text into Positive, Negative, or Neutral sentiments. As a part of this task, we conducted a series of experiments with several pre-trained sequence classification models {--} XLM-RoBERTa, BanglaBERT, Bangla BERT Base and Multilingual BERT. Among these, our best-performing model was based on the XLM-RoBERTa-base architecture, which outperforms baseline models. Our system was ranked 19th among the 30 teams that participated in the task.
Embeddings at BLP-2023 Task 2: Optimizing Fine-Tuned Transformers with Cost-Sensitive Learning for Multiclass Sentiment Analysis (2023)

In this study, we address the task of Sentiment Analysis for Bangla Social Media Posts, introduced in first Workshop on Bangla Language Processing (CITATION). Our research encountered two significant challenges in the context of sentiment analysis. The first challenge involved extensive training times and memory constraints when we chose to employ oversampling techniques for addressing class imbalance in an attempt to enhance model performance. Conversely, when opting for undersampling, the training time was optimal, but this approach resulted in poor model performance. These challenges highlight the complex trade-offs involved in selecting sampling methods to address class imbalances in sentiment analysis tasks. We tackle these challenges through cost-sensitive approaches aimed at enhancing model performance. In our initial submission during the evaluation phase, we ranked 9th out of 30 participants with an F1-micro score of 0.7088 . Subsequently, through additional experimentation, we managed to elevate our F1-micro score to 0.7186 by leveraging the BanglaBERT-Large model in combination with the Self-adjusting Dice loss function. Our experiments highlight the effect in performance of the models achieved by modifying the loss function. Our experimental data and source code can be found here.
LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla Language (2023)

This paper describes the system of the LowResource Team for Task 2 of BLP-2023, which involves conducting sentiment analysis on a dataset composed of public posts and comments from diverse social media platforms. Our primary aim was to utilize BanglaBert, a BERT model pre-trained on a large Bangla corpus, using various strategies including fine-tuning, dropping random tokens, and using several external datasets. Our final model is an ensemble of the three best BanglaBert variations. Our system achieved overall 3rd in the Test Set among 30 participating teams with a score of 0.718. Additionally, we discuss the promising systems that didn`t perform well namely task-adaptive pertaining and paraphrasing using BanglaT5. Our training codes are publicly available at https://github.com/Aunabil4602/bnlp-workshop-task2-2023
BLP-2023 Task 2: Sentiment Analysis (2023)

We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop, co-located with EMNLP 2023. The task is defined as the detection of sentiment in a given piece of social media text. This task attracted interest from 71 participants, among whom 29 and 30 teams submitted systems during the development and evaluation phases, respectively. In total, participants submitted 597 runs. However, only 15 teams submitted system description papers. The range of approaches in the submitted systems spans from classical machine learning models, fine-tuning pre-trained models, to leveraging Large Language Model (LLMs) in zero- and few-shot settings. In this paper, we provide a detailed account of the task setup, including dataset development and evaluation setup. Additionally, we provide a succinct overview of the systems submitted by the participants. All datasets and evaluation scripts from the shared task have been made publicly available for the research community, to foster further research in this domain.
Analyzing the Potential of Linguistic Features for Sign Spotting: A Look at Approximative Features (2023)

Sign language processing is the field of research that aims to recognize, retrieve, and spot signs in videos. Various approaches have been developed, varying in whether they use linguistic features and whether they use landmark detection tools or not. Incorporating linguistics holds promise for improving sign language processing in terms of performance, generalizability, and explainability. This paper focuses on the task of sign spotting and aims to expand on the approximative linguistic features that have been used in previous work, and to understand when linguistic features deliver an improvement over landmark features. We detect landmarks with Mediapipe and extract linguistically relevant features from them, including handshape, orientation, location, and movement. We compare a sign spotting model using linguistic features with a model operating on landmarks directly, finding that the approximate linguistic features tested in this paper capture some aspects of signs better than the landmark features, while they are worse for others.
A Linked Data Approach for linking and aligning Sign Language and Spoken Language Data (2023)

We present work dealing with a Linked Open Data (LOD)-compliant representation of Sign Language (SL) data, with the goal of supporting the cross-lingual alignment of SL data and their linking to Spoken Language (SpL) data. The proposed representation is based on activities of groups of researchers in the field of SL who have investigated the use of Open Multilingual Wordnet (OMW) datasets for (manually) cross-linking SL data or for linking SL and SpL data. Another group of researchers is proposing an XML encoding of articulatory elements of SLs and (manually) linking those to an SpL lexical resource. We propose an RDF-based representation of those various data. This unified formal representation offers a semantic repository of information on SL and SpL data that could be accessed for supporting the creation of datasets for training or evaluating NLP applications dealing with SLs, thinking for example of Machine Translation (MT) between SLs and between SLs and SpLs.
An Open-Source Gloss-Based Baseline for Spoken to Signed Language Translation (2023)

Sign language translation systems are complex and require many components. As a result, it is very hard to compare methods across publications. We present an open-source implementation of a text-to-gloss-to-pose-to-video pipeline approach, demonstrating conversion from German to Swiss German Sign Language, French to French Sign Language of Switzerland, and Italian to Italian Sign Language of Switzerland. We propose three different components for the text-to-gloss translation: a lemmatizer, a rule-based word reordering and dropping component, and a neural machine translation system. Gloss-to-pose conversion occurs using data from a lexicon for three different signed languages, with skeletal poses extracted from videos. To generate a sentence, the text-to-gloss system is first run, and the pose representations of the resulting signs are stitched together.
A New English-Dutch-NGT Corpus for the Hospitality Domain (2023)

One of the major challenges hampering the development of language technology which targets sign languages is the extremely limited availability of good quality data geared towards machine learning and deep learning approaches. In this paper we introduce the NGT-Dutch Hotel Review Corpus (NGT-HoReCo), which addresses this issue by providing multimodal parallel data in English, Dutch and Sign Language of the Netherlands (NGT). The corpus contains 283 hotel reviews in written English, translated into written Dutch and into NGT videos. It will be made publicly available through CLARIN and through the ELG platform.
BSL-Hansard: A parallel, multimodal corpus of English and interpreted British Sign Language data from parliamentary proceedings (2023)

BSL-Hansard is a novel open source and multimodal resource composed by combining Sign Language video data in BSL and English text from the official transcription of British parliamentary sessions. This paper describes the method followed to compile BSL-Hansard including time alignment of text using the MAUS (Schiel, 2015) segmentation system, gives some statistics about this dataset, and suggests experiments. These primarily include end-to-end Sign Language-to-text translation, but is also relevant for broader machine translation, and speech and language processing tasks.
Towards Accommodating Gerunds within the Sign Language Lexicon (2023)

This work is part of ongoing research work that focuses on the linguistic analysis and computational description of five different Sign Languages (SLs), namely Irish Sign Language (ISL), Flemish Sign Language (VGT), Dutch Sign Language (NGT), Spanish Sign Language (LSE), and British Sign Language (BSL). This work will be leveraged to inform the development of SL lexicon entries for a Sign Language Machine Translation (SLMT) system. In particular, this research focuses on ISL. We investigate the existence of constructions similar to or equivalent in functionality to gerunds in spoken language, in particular, English. The initial findings indicate that such constructions do indeed exist and that they can take many forms.
Detecting Argumentative Fallacies in the Wild: Problems and Limitations of Large Language Models (2023)

Previous work on the automatic identification of fallacies in natural language text has typically approached the problem in constrained experimental setups that make it difficult to understand the applicability and usefulness of the proposals in the real world. In this paper, we present the first analysis of the limitations that these data-driven approaches could show in real situations. For that purpose, we first create a validation corpus consisting of natural language argumentation schemes. Second, we provide new empirical results to the emerging task of identifying fallacies in natural language text. Third, we analyse the errors observed outside of the testing data domains considering the new validation corpus. Finally, we point out some important limitations observed in our analysis that should be taken into account in future research in this topic. Specifically, if we want to deploy these systems in the Wild.
Using Masked Language Model Probabilities of Connectives for Stance Detection in English Discourse (2023)

This paper introduces an approach which operationalizes the role of discourse connectives for detecting argument stance. Specifically, the study investigates the utility of masked language model probabilities of discourse connectives inserted between a claim and a premise that supports or attacks it. The research focuses on a range of connectives known to signal support or attack, such as because, but, so, or although. By employing a LightGBM classifier, the study reveals promising results in stance detection in English discourse. While the proposed system does not aim to outperform state-of-the-art architectures, the classification accuracy is surprisingly high, highlighting the potential of these features to enhance argument mining tasks, including stance detection.