When a Language Question Is at Stake. A Revisited Approach to Label Sensitive Content

• URL: http://arxiv.org/abs/2311.10514v1
• Date: Fri, 17 Nov 2023 13:35:10 GMT
• Title: When a Language Question Is at Stake. A Revisited Approach to Label Sensitive Content
• Authors: Stetsenko Daria
• Abstract summary: Article revisits an approach of pseudo-labeling sensitive data on the example of Ukrainian tweets covering the Russian-Ukrainian war. We provide a fundamental statistical analysis of the obtained data, evaluation of models used for pseudo-labelling, and set further guidelines on how the scientists can leverage the corpus.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Many under-resourced languages require high-quality datasets for specific tasks such as offensive language detection, disinformation, or misinformation identification. However, the intricacies of the content may have a detrimental effect on the annotators. The article aims to revisit an approach of pseudo-labeling sensitive data on the example of Ukrainian tweets covering the Russian-Ukrainian war. Nowadays, this acute topic is in the spotlight of various language manipulations that cause numerous disinformation and profanity on social media platforms. The conducted experiment highlights three main stages of data annotation and underlines the main obstacles during machine annotation. Ultimately, we provide a fundamental statistical analysis of the obtained data, evaluation of models used for pseudo-labelling, and set further guidelines on how the scientists can leverage the corpus to execute more advanced research and extend the existing data samples without annotators' engagement.

Related papers

• Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language [2.2823100315094624]
  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.
  arXiv  Detail & Related papers  (2024-07-29T15:02:51Z)
• A multilingual dataset for offensive language and hate speech detection for hausa, yoruba and igbo languages [0.0]
  This study addresses the challenge by developing and introducing novel datasets for offensive language detection in three major Nigerian languages: Hausa, Yoruba, and Igbo. We collected data from Twitter and manually annotated it to create datasets for each of the three languages, using native speakers. We used pre-trained language models to evaluate their efficacy in detecting offensive language in our datasets. The best-performing model achieved an accuracy of 90%.
  arXiv  Detail & Related papers  (2024-06-04T09:58:29Z)
• Understanding Cross-Lingual Alignment -- A Survey [52.572071017877704]
  Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field.
  arXiv  Detail & Related papers  (2024-04-09T11:39:53Z)
• Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation Detection [0.0]
  The detection of misleading content presents a significant hurdle due to its extreme linguistic and domain variability. This paper analyzes the linguistic attributes that characterize this phenomenon and how representative of such features some of the most popular misinformation datasets are. We demonstrate that the appropriate use of pertinent symbolic knowledge in combination with neural language models is helpful in detecting misleading content.
  arXiv  Detail & Related papers  (2024-01-29T16:42:34Z)
• Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization [57.38123229553157]
  This paper presents an effective transfer learning framework for language adaptation in text-to-speech systems. We focus on achieving language adaptation using minimal labeled and unlabeled data. Experimental results show that our framework is able to synthesize intelligible speech in unseen languages with only 4 utterances of labeled data and 15 minutes of unlabeled data.
  arXiv  Detail & Related papers  (2024-01-23T21:55:34Z)
• SER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults [58.49386651361823]
  SER_AMPEL is a multi-source dataset for speech emotion recognition (SER) It is collected with the aim of providing a reference for speech emotion recognition in case of Italian older adults. The evidence of the need for such a dataset emerges from the analysis of the state of the art.
  arXiv  Detail & Related papers  (2023-11-24T13:47:25Z)
• On Guiding Visual Attention with Language Specification [76.08326100891571]
  We use high-level language specification as advice for constraining the classification evidence to task-relevant features, instead of distractors. We show that supervising spatial attention in this way improves performance on classification tasks with biased and noisy data.
  arXiv  Detail & Related papers  (2022-02-17T22:40:19Z)
• Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation [70.81596088969378]
  Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding. COD enables dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages.
  arXiv  Detail & Related papers  (2022-01-31T18:11:21Z)
• Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement [7.288480094345606]
  We focus on the level of agreement among annotators while selecting data to create offensive language datasets. Our study comprises the creation of three novel datasets of English tweets covering different topics. We show that such hard cases, where low agreement is present, are not necessarily due to poor-quality annotation.
  arXiv  Detail & Related papers  (2021-09-28T08:55:04Z)
• Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter [9.359018642178917]
  This paper presents a method to obtain multilingual datasets for stance detection in Twitter. We leverage user-based information to semi-automatically label large amounts of tweets.
  arXiv  Detail & Related papers  (2021-01-28T13:05:09Z)
• Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification [52.69730591919885]
  We present a semi-supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. We observe significant gains in effectiveness on document and intent classification for a diverse set of languages.
  arXiv  Detail & Related papers  (2020-07-29T19:38:35Z)

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.