User-Centric Gender Rewriting

• URL: http://arxiv.org/abs/2205.02211v1
• Date: Wed, 4 May 2022 17:46:17 GMT
• Title: User-Centric Gender Rewriting
• Authors: Bashar Alhafni, Nizar Habash, Houda Bouamor
• Abstract summary: We define the task of gender rewriting in contexts involving two users (I and/or You) We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models. Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting.
• Score: 12.519348416773553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we define the task of gender rewriting in contexts involving two users (I and/or You) - first and second grammatical persons with independent grammatical gender preferences. We focus on Arabic, a gender-marking morphologically rich language. We develop a multi-step system that combines the positive aspects of both rule-based and neural rewriting models. Our results successfully demonstrate the viability of this approach on a recently created corpus for Arabic gender rewriting, achieving 88.42 M2 F0.5 on a blind test set. Our proposed system improves over previous work on the first-person-only version of this task, by 3.05 absolute increase in M2 F0.5. We demonstrate a use case of our gender rewriting system by using it to post-edit the output of a commercial MT system to provide personalized outputs based on the users' grammatical gender preferences. We make our code, data, and models publicly available.

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)
• What is Your Favorite Gender, MLM? Gender Bias Evaluation in Multilingual Masked Language Models [8.618945530676614]
  This paper proposes an approach to estimate gender bias in multilingual lexicons from 5 languages: Chinese, English, German, Portuguese, and Spanish. A novel model-based method is presented to generate sentence pairs for a more robust analysis of gender bias. Our results suggest that gender bias should be studied on a large dataset using multiple evaluation metrics for best practice.
  arXiv  Detail & Related papers  (2024-04-09T21:12:08Z)
• Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You [64.74707085021858]
  We show that multilingual models suffer from significant gender biases just as monolingual models do. We propose a novel benchmark, MAGBIG, intended to foster research on gender bias in multilingual models. Our results show that not only do models exhibit strong gender biases but they also behave differently across languages.
  arXiv  Detail & Related papers  (2024-01-29T12:02:28Z)
• How To Build Competitive Multi-gender Speech Translation Models For Controlling Speaker Gender Translation [21.125217707038356]
  When translating from notional gender languages into grammatical gender languages, the generated translation requires explicit gender assignments for various words, including those referring to the speaker. To avoid such biased and not inclusive behaviors, the gender assignment of speaker-related expressions should be guided by externally-provided metadata about the speaker's gender. This paper aims to achieve the same results by integrating the speaker's gender metadata into a single "multi-gender" neural ST model, easier to maintain.
  arXiv  Detail & Related papers  (2023-10-23T17:21:32Z)
• 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)
• Auditing Gender Presentation Differences in Text-to-Image Models [54.16959473093973]
  We study how gender is presented differently in text-to-image models. By probing gender indicators in the input text, we quantify the frequency differences of presentation-centric attributes. We propose an automatic method to estimate such differences.
  arXiv  Detail & Related papers  (2023-02-07T18:52:22Z)
• The User-Aware Arabic Gender Rewriter [12.26152421175485]
  We introduce the User-Aware Arabic Gender Rewriter, a user-centric web-based system for Arabic gender rewriting. The system takes either Arabic or English sentences as input, and provides users with the ability to specify their desired first and/or second person target genders.
  arXiv  Detail & Related papers  (2022-10-14T05:34:57Z)
• The Arabic Parallel Gender Corpus 2.0: Extensions and Analyses [17.253633576291897]
  We introduce a new corpus for gender identification and rewriting in contexts involving one or two target users. We focus on Arabic, a gender-marking morphologically rich language.
  arXiv  Detail & Related papers  (2021-10-18T12:06:17Z)
• Generating Gender Augmented Data for NLP [3.5557219875516655]
  Gender bias is a frequent occurrence in NLP-based applications, especially in gender-inflected languages. This paper proposes an automatic and generalisable rewriting approach for short conversational sentences. The proposed approach is based on a neural machine translation (NMT) system trained to 'translate' from one gender alternative to another.
  arXiv  Detail & Related papers  (2021-07-13T11:13:21Z)
• They, Them, Theirs: Rewriting with Gender-Neutral English [56.14842450974887]
  We perform a case study on the singular they, a common way to promote gender inclusion in English. We show how a model can be trained to produce gender-neutral English with 1% word error rate with no human-labeled data.
  arXiv  Detail & Related papers  (2021-02-12T21:47:48Z)
• 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.