Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language

• URL: http://arxiv.org/abs/2407.20076v1
• Date: Mon, 29 Jul 2024 15:02:51 GMT
• Title: Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language
• Authors: Elena Beatrice Nicola, Dumitru Clementin Cercel, Florin Pop,
• Abstract summary: Offensive language detection is a crucial task in today's digital landscape. Building robust offensive language detection models requires large amounts of labeled data. Semi-supervised learning offers a feasible solution by utilizing labeled and unlabeled data.
• Score: 2.2823100315094624
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Offensive language detection is a crucial task in today's digital landscape, where online platforms grapple with maintaining a respectful and inclusive environment. However, building robust offensive language detection models requires large amounts of labeled data, which can be expensive and time-consuming to obtain. Semi-supervised learning offers a feasible solution by utilizing labeled and unlabeled data to create more accurate and robust models. In this paper, we explore a few different semi-supervised methods, as well as data augmentation techniques. Concretely, we implemented eight semi-supervised methods and ran experiments for them using only the available data in the RO-Offense dataset and applying five augmentation techniques before feeding the data to the models. Experimental results demonstrate that some of them benefit more from augmentations than others.

Related papers

• Unsupervised Data Validation Methods for Efficient Model Training [0.0]
  State-of-the-art models in natural language processing (NLP), text-to-speech (TTS), speech-to-text (STT) and vision-language models (VLM) rely heavily on large datasets. This research explores key areas such as defining "quality data," developing methods for generating appropriate data and enhancing accessibility to model training.
  arXiv  Detail & Related papers  (2024-10-10T13:00:53Z)
• Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution [7.681258910515419]
  Tabular data presents unique challenges due to its heterogeneous nature and complex structural relationships. High predictive performance and robustness in tabular data analysis holds significant promise for numerous applications. The recent advent of large language models, such as GPT and LLaMA, has further revolutionized the field, facilitating more advanced and diverse applications with minimal fine-tuning.
  arXiv  Detail & Related papers  (2024-08-20T04:59:19Z)
• Capture the Flag: Uncovering Data Insights with Large Language Models [90.47038584812925]
  This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data. We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset.
  arXiv  Detail & Related papers  (2023-12-21T14:20:06Z)
• Dissecting vocabulary biases datasets through statistical testing and automated data augmentation for artifact mitigation in Natural Language Inference [3.154631846975021]
  We focus on investigating dataset artifacts and developing strategies to address these issues. We propose several automatic data augmentation strategies spanning character to word levels. Experiments demonstrate that the proposed approaches effectively enhance model accuracy and reduce biases by up to 0.66% and 1.14%, respectively.
  arXiv  Detail & Related papers  (2023-12-14T08:46:26Z)
• AugGPT: Leveraging ChatGPT for Text Data Augmentation [59.76140039943385]
  We propose a text data augmentation approach based on ChatGPT (named AugGPT) AugGPT rephrases each sentence in the training samples into multiple conceptually similar but semantically different samples. Experiment results on few-shot learning text classification tasks show the superior performance of the proposed AugGPT approach.
  arXiv  Detail & Related papers  (2023-02-25T06:58:16Z)
• Sample Efficient Approaches for Idiomaticity Detection [6.481818246474555]
  This work explores sample efficient methods of idiomaticity detection. In particular, we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings. Our experiments show that whilePET improves performance on English, they are much less effective on Portuguese and Galician, leading to an overall performance about on par with vanilla mBERT.
  arXiv  Detail & Related papers  (2022-05-23T13:46:35Z)
• Evaluating BERT-based Pre-training Language Models for Detecting Misinformation [2.1915057426589746]
  It is challenging to control the quality of online information due to the lack of supervision over all the information posted online. There is a need for automated rumour detection techniques to limit the adverse effects of spreading misinformation. This study proposes the BERT-based pre-trained language models to encode text data into vectors and utilise neural network models to classify these vectors to detect misinformation.
  arXiv  Detail & Related papers  (2022-03-15T08:54:36Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• Efficient Nearest Neighbor Language Models [114.40866461741795]
  Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore. We show how to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
  arXiv  Detail & Related papers  (2021-09-09T12:32:28Z)
• Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding [69.40915115518523]
  Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Various data augmentation approaches have been proposed to synthesize training data in low-resource target languages. In this paper we focus on mitigating noise in augmented data.
  arXiv  Detail & Related papers  (2021-09-03T15:44:15Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.