Behind Closed Words: Creating and Investigating the forePLay Annotated Dataset for Polish Erotic Discourse

• URL: http://arxiv.org/abs/2412.17533v2
• Date: Tue, 07 Jan 2025 12:56:27 GMT
• Title: Behind Closed Words: Creating and Investigating the forePLay Annotated Dataset for Polish Erotic Discourse
• Authors: Anna Kołos, Katarzyna Lorenc, Emilia Wiśnios, Agnieszka Karlińska,
• Abstract summary: We present forePLay, a novel Polish language dataset for erotic content detection. This dataset features over 24k annotated sentences with a multidimensional taxonomy encompassing ambiguity, violence, and social unacceptability dimensions.
• Score: 0.0
• License:
• Abstract: The surge in online content has created an urgent demand for robust detection systems, especially in non-English contexts where current tools demonstrate significant limitations. We present forePLay, a novel Polish language dataset for erotic content detection, featuring over 24k annotated sentences with a multidimensional taxonomy encompassing ambiguity, violence, and social unacceptability dimensions. Our comprehensive evaluation demonstrates that specialized Polish language models achieve superior performance compared to multilingual alternatives, with transformer-based architectures showing particular strength in handling imbalanced categories. The dataset and accompanying analysis establish essential frameworks for developing linguistically-aware content moderation systems, while highlighting critical considerations for extending such capabilities to morphologically complex languages.

Related papers

• From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models [17.04716417556556]
  This review visits foundational concepts such as the distributional hypothesis and contextual similarity. We examine both static and contextualized embeddings, underscoring advancements in models such as ELMo, BERT, and GPT. The discussion extends to sentence and document embeddings, covering aggregation methods and generative topic models. Advanced topics such as model compression, interpretability, numerical encoding, and bias mitigation are analyzed, addressing both technical challenges and ethical implications.
  arXiv  Detail & Related papers  (2024-11-06T15:40:02Z)
• Harnessing the Intrinsic Knowledge of Pretrained Language Models for Challenging Text Classification Settings [5.257719744958367]
  This thesis explores three challenging settings in text classification by leveraging the intrinsic knowledge of pretrained language models (PLMs) We develop models that utilize features based on contextualized word representations from PLMs, achieving performance that rivals or surpasses human accuracy. Lastly, we tackle the sensitivity of large language models to in-context learning prompts by selecting effective demonstrations.
  arXiv  Detail & Related papers  (2024-08-28T09:07:30Z)
• 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)
• Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models [0.08246494848934444]
  We focus on dialogues within Tunisian social networks during the Coronavirus Pandemic and other notable themes like sports and politics. We start by aggregating a varied multilingual corpus of comments relevant to these subjects. We then introduce our No-English-to-English Machine Translation approach to handle linguistic differences.
  arXiv  Detail & Related papers  (2024-03-18T00:01:10Z)
• A comprehensive cross-language framework for harmful content detection with the aid of sentiment analysis [0.356008609689971]
  This study introduces, for the first time, a detailed framework adaptable to any language. A key component of the framework is the development of a general and detailed annotation guideline. The integration of sentiment analysis represents a novel approach to enhancing harmful language detection.
  arXiv  Detail & Related papers  (2024-03-02T17:13:47Z)
• When a Language Question Is at Stake. A Revisited Approach to Label Sensitive Content [0.0]
  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.
  arXiv  Detail & Related papers  (2023-11-17T13:35:10Z)
• Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers [86.64972552583941]
  We put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
  arXiv  Detail & Related papers  (2022-05-23T16:47:37Z)
• A New Generation of Perspective API: Efficient Multilingual Character-level Transformers [66.9176610388952]
  We present the fundamentals behind the next version of the Perspective API from Google Jigsaw. At the heart of the approach is a single multilingual token-free Charformer model. We demonstrate that by forgoing static vocabularies, we gain flexibility across a variety of settings.
  arXiv  Detail & Related papers  (2022-02-22T20:55:31Z)
• 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)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• Linguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures [73.06435180872293]
  We construct a recurrent neural network predictor based on byte embeddings and convolutional layers. We show that some features from various linguistic types can be predicted reliably.
  arXiv  Detail & Related papers  (2020-04-30T21:00:53Z)

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.