nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla
Sentiment Analysis
- URL: http://arxiv.org/abs/2311.15032v1
- Date: Sat, 25 Nov 2023 13:58:58 GMT
- Title: nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla
Sentiment Analysis
- Authors: Dhiman Goswami, Md Nishat Raihan, Sadiya Sayara Chowdhury Puspo,
Marcos Zampieri
- Abstract summary: In this paper, we discuss the nlpBDpatriots entry to the shared task on Sentiment Analysis of Bangla Social Media Posts.
The main objective of this task is to identify the polarity of social media content using a Bangla dataset annotated with positive, neutral, and negative labels.
Our best system ranked 12th among 30 teams that participated in the competition.
- Score: 7.3481279783709805
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this paper, we discuss the nlpBDpatriots entry to the shared task on
Sentiment Analysis of Bangla Social Media Posts organized at the first workshop
on Bangla Language Processing (BLP) co-located with EMNLP. The main objective
of this task is to identify the polarity of social media content using a Bangla
dataset annotated with positive, neutral, and negative labels provided by the
shared task organizers. Our best system for this task is a transfer learning
approach with data augmentation which achieved a micro F1 score of 0.71. Our
best system ranked 12th among 30 teams that participated in the competition.
Related papers
- nlpBDpatriots at BLP-2023 Task 1: A Two-Step Classification for Violence
Inciting Text Detection in Bangla [7.3481279783709805]
In this paper, we discuss the nlpBDpatriots entry to the shared task on Violence Inciting Text Detection (VITD)
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 6th out of 27 teams with a macro F1 score of 0.74.
arXiv Detail & Related papers (2023-11-25T13:47:34Z) - LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource
Sentiment Analysis of Bangla Language [0.5922488908114022]
This paper describes the system of the LowResource Team for Task 2 of BLP-2023.
It involves conducting sentiment analysis on a dataset composed of public posts and comments from diverse social media platforms.
Our primary aim is to utilize BanglaBert, a BERT model pre-trained on a large Bangla corpus.
arXiv Detail & Related papers (2023-11-21T17:21:15Z) - BLP-2023 Task 2: Sentiment Analysis [7.725694295666573]
We present an overview of the BLP Sentiment Shared Task, organized as part of the inaugural BLP 2023 workshop.
The task is defined as the detection of sentiment in a given piece of social media text.
This paper provides a detailed account of the task setup, including dataset development and evaluation setup.
arXiv Detail & Related papers (2023-10-24T21:00:41Z) - RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and
Majority Voted Fine-Tuned Transformers [2.048226951354646]
This paper describes our approach to submissions made at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts.
Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task.
arXiv Detail & Related papers (2023-10-22T10:55:56Z) - NICE: Improving Panoptic Narrative Detection and Segmentation with
Cascading Collaborative Learning [77.95710025273218]
We propose a unified framework called NICE that can jointly learn two panoptic narrative recognition tasks.
By linking PNS and PND in series with the barycenter of segmentation as the anchor, our approach naturally aligns the two tasks.
NICE surpasses all existing methods by a large margin, achieving 4.1% for PND and 2.9% for PNS over the state-of-the-art.
arXiv Detail & Related papers (2023-10-17T03:42:12Z) - Ranking-based Group Identification via Factorized Attention on Social
Tripartite Graph [68.08590487960475]
We propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG)
We devise tripartite graph convolution layers to aggregate information from different types of neighborhoods among users, groups, and items.
To cope with the data sparsity issue, we devise a novel propagation augmentation layer, which is based on our proposed factorized attention mechanism.
arXiv Detail & Related papers (2022-11-02T01:42:20Z) - Heterogeneous Federated Learning via Grouped Sequential-to-Parallel
Training [60.892342868936865]
Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm.
We propose a data heterogeneous-robust FL approach, FedGSP, to address this challenge.
We show that FedGSP improves the accuracy by 3.7% on average compared with seven state-of-the-art approaches.
arXiv Detail & Related papers (2022-01-31T03:15:28Z) - Dimsum @LaySumm 20: BART-based Approach for Scientific Document
Summarization [50.939885303186195]
We build a lay summary generation system based on the BART model.
We leverage sentence labels as extra supervision signals to improve the performance of lay summarization.
arXiv Detail & Related papers (2020-10-19T06:36:11Z) - SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual
Media [50.29389719723529]
We present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media.
The goal of this shared task is to design automatic methods for emphasis selection.
The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used.
arXiv Detail & Related papers (2020-08-07T17:24:53Z) - Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to
Code-Mixed Sentiment Analysis [1.2147145617662432]
We explore the task of sentiment analysis on Hinglish (code-mixed Hindi-English) tweets as participants of Task 9 of the SemEval-2020 competition, known as the SentiMix task.
We had two main approaches: 1) applying transfer learning by fine-tuning pre-trained BERT models and 2) training feedforward neural networks on bag-of-words representations.
During the evaluation phase of the competition, we obtained an F-score of 71.3% with our best model, which placed $4th$ out of 62 entries in the official system rankings.
arXiv Detail & Related papers (2020-07-26T05:48:46Z) - Kungfupanda at SemEval-2020 Task 12: BERT-Based Multi-Task Learning for
Offensive Language Detection [55.445023584632175]
We build an offensive language detection system, which combines multi-task learning with BERT-based models.
Our model achieves 91.51% F1 score in English Sub-task A, which is comparable to the first place.
arXiv Detail & Related papers (2020-04-28T11:27:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.