Beyond Hate Speech: NLP's Challenges and Opportunities in Uncovering Dehumanizing Language

• URL: http://arxiv.org/abs/2402.13818v1
• Date: Wed, 21 Feb 2024 13:57:36 GMT
• Title: Beyond Hate Speech: NLP's Challenges and Opportunities in Uncovering Dehumanizing Language
• Authors: Hezhao Zhang, Lasana Harris, Nafise Sadat Moosavi
• Abstract summary: This paper evaluates the performance of cutting-edge NLP models, including GPT-4, GPT-3.5, and LLAMA-2 in identifying dehumanizing language. Our findings reveal that while these models demonstrate potential, achieving a 70% accuracy rate in distinguishing dehumanizing language from broader hate speech, they also display biases.
• Score: 11.946719280041789
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dehumanization, characterized as a subtle yet harmful manifestation of hate speech, involves denying individuals of their human qualities and often results in violence against marginalized groups. Despite significant progress in Natural Language Processing across various domains, its application in detecting dehumanizing language is limited, largely due to the scarcity of publicly available annotated data for this domain. This paper evaluates the performance of cutting-edge NLP models, including GPT-4, GPT-3.5, and LLAMA-2, in identifying dehumanizing language. Our findings reveal that while these models demonstrate potential, achieving a 70\% accuracy rate in distinguishing dehumanizing language from broader hate speech, they also display biases. They are over-sensitive in classifying other forms of hate speech as dehumanization for a specific subset of target groups, while more frequently failing to identify clear cases of dehumanization for other target groups. Moreover, leveraging one of the best-performing models, we automatically annotated a larger dataset for training more accessible models. However, our findings indicate that these models currently do not meet the high-quality data generation threshold necessary for this task.

Related papers

• A Target-Aware Analysis of Data Augmentation for Hate Speech Detection [3.858155067958448]
  Hate speech is one of the main threats posed by the widespread use of social networks. We investigate the possibility of augmenting existing data with generative language models, reducing target imbalance. For some hate categories such as origin, religion, and disability, hate speech classification using augmented data for training improves by more than 10% F1 over the no augmentation baseline.
  arXiv  Detail & Related papers  (2024-10-10T15:46:27Z)
• Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models [50.40276881893513]
  This study introduces Spoken Stereoset, a dataset specifically designed to evaluate social biases in Speech Large Language Models (SLLMs) By examining how different models respond to speech from diverse demographic groups, we aim to identify these biases. The findings indicate that while most models show minimal bias, some still exhibit slightly stereotypical or anti-stereotypical tendencies.
  arXiv  Detail & Related papers  (2024-08-14T16:55:06Z)
• HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments [2.162419921663162]
  We propose a novel end-to-end model, HCDIR, for Hate Context Detection, and Hate Intensity Reduction in social media posts. We fine-tuned several pre-trained language models to detect hateful comments to ascertain the best-performing hateful comments detection model.
  arXiv  Detail & Related papers  (2023-12-20T17:05:46Z)
• Developing Linguistic Patterns to Mitigate Inherent Human Bias in Offensive Language Detection [1.6574413179773761]
  We propose a linguistic data augmentation approach to reduce bias in labeling processes. This approach has the potential to improve offensive language classification tasks across multiple languages.
  arXiv  Detail & Related papers  (2023-12-04T10:20:36Z)
• Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection [23.97444551607624]
  Hate speech in social media is a growing phenomenon, and detecting such toxic content has gained significant traction. HateMAML is a model-agnostic meta-learning-based framework that effectively performs hate speech detection in low-resource languages. Extensive experiments are conducted on five datasets across eight different low-resource languages.
  arXiv  Detail & Related papers  (2023-03-04T22:28:29Z)
• Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
  Figurative and metaphorical language are commonplace in discourse. It remains an open question to what extent modern language models can interpret nonliteral phrases. We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
  arXiv  Detail & Related papers  (2022-04-26T23:42:22Z)
• Addressing the Challenges of Cross-Lingual Hate Speech Detection [115.1352779982269]
  In this paper we focus on cross-lingual transfer learning to support hate speech detection in low-resource languages. We leverage cross-lingual word embeddings to train our neural network systems on the source language and apply it to the target language. We investigate the issue of label imbalance of hate speech datasets, since the high ratio of non-hate examples compared to hate examples often leads to low model performance.
  arXiv  Detail & Related papers  (2022-01-15T20:48:14Z)
• Analyzing the Limits of Self-Supervision in Handling Bias in Language [52.26068057260399]
  We evaluate how well language models capture the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing. Our analyses indicate that language models are capable of performing these tasks to widely varying degrees across different bias dimensions, such as gender and political affiliation.
  arXiv  Detail & Related papers  (2021-12-16T05:36:08Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Cross-lingual hate speech detection based on multilingual domain-specific word embeddings [4.769747792846004]
  We propose to address the problem of multilingual hate speech detection from the perspective of transfer learning. Our goal is to determine if knowledge from one particular language can be used to classify other language. We show that the use of our simple yet specific multilingual hate representations improves classification results.
  arXiv  Detail & Related papers  (2021-04-30T02:24:50Z)
• A Framework for the Computational Linguistic Analysis of Dehumanization [52.735780962665814]
  We analyze discussions of LGBTQ people in the New York Times from 1986 to 2015. We find increasingly humanizing descriptions of LGBTQ people over time. The ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
  arXiv  Detail & Related papers  (2020-03-06T03:02:12Z)

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.