A Multilingual Perspective on Probing Gender Bias

• URL: http://arxiv.org/abs/2403.10699v1
• Date: Fri, 15 Mar 2024 21:35:21 GMT
• Title: A Multilingual Perspective on Probing Gender Bias
• Authors: Karolina Stańczak,
• Abstract summary: Gender bias is a form of systematic negative treatment that targets individuals based on their gender. This thesis investigates the nuances of how gender bias is expressed through language and within language technologies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gender bias represents a form of systematic negative treatment that targets individuals based on their gender. This discrimination can range from subtle sexist remarks and gendered stereotypes to outright hate speech. Prior research has revealed that ignoring online abuse not only affects the individuals targeted but also has broader societal implications. These consequences extend to the discouragement of women's engagement and visibility within public spheres, thereby reinforcing gender inequality. This thesis investigates the nuances of how gender bias is expressed through language and within language technologies. Significantly, this thesis expands research on gender bias to multilingual contexts, emphasising the importance of a multilingual and multicultural perspective in understanding societal biases. In this thesis, I adopt an interdisciplinary approach, bridging natural language processing with other disciplines such as political science and history, to probe gender bias in natural language and language models.

Related papers

• Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
  Existing machine translation gender bias evaluations are primarily focused on male and female genders. This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words) We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
  arXiv  Detail & Related papers  (2024-07-23T08:13:51Z)
• Akal Badi ya Bias: An Exploratory Study of Gender Bias in Hindi Language Technology [22.458957168929487]
  Existing research in measuring and mitigating gender bias predominantly centers on English. This paper presents the first comprehensive study delving into the nuanced landscape of gender bias in Hindi.
  arXiv  Detail & Related papers  (2024-05-10T09:26:12Z)
• Politeness Stereotypes and Attack Vectors: Gender Stereotypes in Japanese and Korean Language Models [1.5039745292757671]
  We study how grammatical gender bias relating to politeness levels manifests in Japanese and Korean language models. We find that informal polite speech is most indicative of the female grammatical gender, while rude and formal speech is most indicative of the male grammatical gender. We find politeness levels to be an attack vector for allocational gender bias in cyberbullying detection models.
  arXiv  Detail & Related papers  (2023-06-16T10:36:18Z)
• Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation [12.376309678270275]
  bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.
  arXiv  Detail & Related papers  (2023-05-26T13:51:50Z)
• Comparing Biases and the Impact of Multilingual Training across Multiple Languages [70.84047257764405]
  We present a bias analysis across Italian, Chinese, English, Hebrew, and Spanish on the downstream sentiment analysis task. We adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for four attributes: race, religion, nationality, and gender. Our results reveal similarities in bias expression such as favoritism of groups that are dominant in each language's culture.
  arXiv  Detail & Related papers  (2023-05-18T18:15:07Z)
• "I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation [69.25368160338043]
  Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation. We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
  arXiv  Detail & Related papers  (2023-05-17T04:21:45Z)
• Efficient Gender Debiasing of Pre-trained Indic Language Models [0.0]
  The gender bias present in the data on which language models are pre-trained gets reflected in the systems that use these models. In our paper, we measure gender bias associated with occupations in Hindi language models. Our results reflect that the bias is reduced post-introduction of our proposed mitigation techniques.
  arXiv  Detail & Related papers  (2022-09-08T09:15:58Z)
• Analyzing Gender Representation in Multilingual Models [59.21915055702203]
  We focus on the representation of gender distinctions as a practical case study. We examine the extent to which the gender concept is encoded in shared subspaces across different languages.
  arXiv  Detail & Related papers  (2022-04-20T00:13:01Z)
• Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models [104.41668491794974]
  We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
  arXiv  Detail & Related papers  (2021-04-15T15:03:26Z)
• Language, communication and society: a gender based linguistics analysis [0.0]
  The purpose of this study is to find evidence for supporting the hypothesis that language is the mirror of our thinking. The answers have been analysed to see if gender stereotypes were present such as the attribution of psychological and behavioural characteristics.
  arXiv  Detail & Related papers  (2020-07-14T08:38:37Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

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.