Does Context Help Mitigate Gender Bias in Neural Machine Translation?

• URL: http://arxiv.org/abs/2406.12364v1
• Date: Tue, 18 Jun 2024 07:44:13 GMT
• Title: Does Context Help Mitigate Gender Bias in Neural Machine Translation?
• Authors: Harritxu Gete, Thierry Etchegoyhen,
• Abstract summary: We analyse the translation of stereotypical professions in English to German, and translation with non-informative context in Basque to Spanish. Our results show that, although context-aware models can significantly enhance translation accuracy for feminine terms, they can still maintain or even amplify gender bias.
• Score: 1.534667887016089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural Machine Translation models tend to perpetuate gender bias present in their training data distribution. Context-aware models have been previously suggested as a means to mitigate this type of bias. In this work, we examine this claim by analysing in detail the translation of stereotypical professions in English to German, and translation with non-informative context in Basque to Spanish. Our results show that, although context-aware models can significantly enhance translation accuracy for feminine terms, they can still maintain or even amplify gender bias. These results highlight the need for more fine-grained approaches to bias mitigation in Neural Machine Translation.

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)
• A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation [35.44115368160656]
  We investigate whether and to what extent machine translation models exhibit gender bias. We find that IFT models default to male-inflected translations, even disregarding female occupational stereotypes. We propose an easy-to-implement and effective bias mitigation solution.
  arXiv  Detail & Related papers  (2023-10-18T17:36:55Z)
• Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts [87.62403265382734]
  Recent studies show that traditional fairytales are rife with harmful gender biases. This work aims to assess learned biases of language models by evaluating their robustness against gender perturbations.
  arXiv  Detail & Related papers  (2023-10-16T22:25:09Z)
• The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated [70.23064111640132]
  We compare the impact of debiasing on performance across multiple downstream tasks using a wide-range of benchmark datasets. Experiments show that the effects of debiasing are consistently emphunderestimated across all tasks.
  arXiv  Detail & Related papers  (2023-09-16T20:25:34Z)
• 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)
• The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning [50.320178219081484]
  We propose an active learning approach that exploits the strengths of both human and machine translations. An ideal utterance selection can significantly reduce the error and bias in the translated data.
  arXiv  Detail & Related papers  (2023-05-22T05:57:47Z)
• How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts [11.684346035745975]
  A growing number of studies highlight the inherent gender bias that Neural Machine Translation models incorporate during training. We investigate whether these models can be instructed to fix their bias during inference using targeted, guided instructions as contexts. We observe large improvements in reducing the gender bias in translations, across three popular test suites.
  arXiv  Detail & Related papers  (2022-05-22T06:31:54Z)
• DEEP: DEnoising Entity Pre-training for Neural Machine Translation [123.6686940355937]
  It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. We propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences.
  arXiv  Detail & Related papers  (2021-11-14T17:28:09Z)
• 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)
• First the worst: Finding better gender translations during beam search [19.921216907778447]
  We focus on gender bias resulting from systematic errors in grammatical gender translation. We experiment with reranking nbest lists using gender features obtained automatically from the source sentence. We find that a combination of these techniques allows large gains in WinoMT accuracy without requiring additional bilingual data or an additional NMT model.
  arXiv  Detail & Related papers  (2021-04-15T12:53:30Z)
• 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.