Are All Spanish Doctors Male? Evaluating Gender Bias in German Machine Translation

• URL: http://arxiv.org/abs/2502.19104v2
• Date: Fri, 28 Feb 2025 15:00:01 GMT
• Title: Are All Spanish Doctors Male? Evaluating Gender Bias in German Machine Translation
• Authors: Michelle Kappl,
• Abstract summary: WinoMTDE is a new gender bias evaluation test set designed to assess occupational stereotyping and underrepresentation in German machine translation systems.<n>The dataset comprises 288 German sentences that are balanced in regard to gender, as well as stereotype, which was annotated using German labor statistics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present WinoMTDE, a new gender bias evaluation test set designed to assess occupational stereotyping and underrepresentation in German machine translation (MT) systems. Building on the automatic evaluation method introduced by arXiv:1906.00591v1, we extend the approach to German, a language with grammatical gender. The WinoMTDE dataset comprises 288 German sentences that are balanced in regard to gender, as well as stereotype, which was annotated using German labor statistics. We conduct a large-scale evaluation of five widely used MT systems and a large language model. Our results reveal persistent bias in most models, with the LLM outperforming traditional systems. The dataset and evaluation code are publicly available under https://github.com/michellekappl/mt_gender_german.

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)
• GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing [72.0343083866144]
  This paper introduces the GenderBias-emphVL benchmark to evaluate occupation-related gender bias in Large Vision-Language Models. Using our benchmark, we extensively evaluate 15 commonly used open-source LVLMs and state-of-the-art commercial APIs. Our findings reveal widespread gender biases in existing LVLMs.
  arXiv  Detail & Related papers  (2024-06-30T05:55:15Z)
• Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into German [17.924716793621627]
  We study gender-fair language in English-to-German machine translation (MT) We conduct the first benchmark study involving two commercial systems and six neural MT models. Our findings show that most systems produce mainly masculine forms and rarely gender-neutral variants.
  arXiv  Detail & Related papers  (2024-06-10T09:39:19Z)
• Gender Inflected or Bias Inflicted: On Using Grammatical Gender Cues for Bias Evaluation in Machine Translation [0.0]
  We use Hindi as the source language and construct two sets of gender-specific sentences to evaluate different Hindi-English (HI-EN) NMT systems. Our work highlights the importance of considering the nature of language when designing such extrinsic bias evaluation datasets.
  arXiv  Detail & Related papers  (2023-11-07T07:09:59Z)
• VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution [80.57383975987676]
  VisoGender is a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas. We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
  arXiv  Detail & Related papers  (2023-06-21T17:59:51Z)
• Social Biases in Automatic Evaluation Metrics for NLG [53.76118154594404]
  We propose an evaluation method based on Word Embeddings Association Test (WEAT) and Sentence Embeddings Association Test (SEAT) to quantify social biases in evaluation metrics. We construct gender-swapped meta-evaluation datasets to explore the potential impact of gender bias in image caption and text summarization tasks.
  arXiv  Detail & Related papers  (2022-10-17T08:55:26Z)
• Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation [64.65911758042914]
  We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
  arXiv  Detail & Related papers  (2022-05-19T20:05:02Z)
• Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation [10.542861450223128]
  We find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments in corpora from three domains. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models.
  arXiv  Detail & Related papers  (2021-09-08T18:14:11Z)
• Unmasking Contextual Stereotypes: Measuring and Mitigating BERT's Gender Bias [12.4543414590979]
  Contextualized word embeddings have been replacing standard embeddings in NLP systems. We measure gender bias by studying associations between gender-denoting target words and names of professions in English and German. We show that our method of measuring bias is appropriate for languages with a rich and gender-marking, such as German.
  arXiv  Detail & Related papers  (2020-10-27T18:06:09Z)
• The LMU Munich System for the WMT 2020 Unsupervised Machine Translation Shared Task [125.06737861979299]
  This paper describes the submission of LMU Munich to the WMT 2020 unsupervised shared task, in two language directions. Our core unsupervised neural machine translation (UNMT) system follows the strategy of Chronopoulou et al. We ensemble our best-performing systems and reach a BLEU score of 32.4 on German->Upper Sorbian and 35.2 on Upper Sorbian->German.
  arXiv  Detail & Related papers  (2020-10-25T19:04:03Z)
• Gender Coreference and Bias Evaluation at WMT 2020 [16.814151914000856]
  Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.
  arXiv  Detail & Related papers  (2020-10-12T20:42:21Z)
• 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)
• Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem [21.44025591721678]
  Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men. Recent WinoMT challenge set allows us to measure this effect directly. We use transfer learning on a small set of trusted, gender-balanced examples.
  arXiv  Detail & Related papers  (2020-04-09T11:55:13Z)

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.