Less than one percent of words would be affected by gender-inclusive
language in German press texts
- URL: http://arxiv.org/abs/2402.03870v1
- Date: Tue, 6 Feb 2024 10:32:34 GMT
- Title: Less than one percent of words would be affected by gender-inclusive
language in German press texts
- Authors: Carolin M\"uller-Spitzer, Samira Ochs, Alexander Koplenig, Jan-Oliver
R\"udiger, Sascha Wolfer
- Abstract summary: We show that, on average, less than 1% of all tokens would be affected by gender-inclusive language.
This small proportion calls into question whether gender-inclusive German presents a substantial barrier to understanding and learning the language.
- Score: 43.16629507708997
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Research on gender and language is tightly knitted to social debates on
gender equality and non-discriminatory language use. Psycholinguistic scholars
have made significant contributions in this field. However, corpus-based
studies that investigate these matters within the context of language use are
still rare. In our study, we address the question of how much textual material
would actually have to be changed if non-gender-inclusive texts were rewritten
to be gender-inclusive. This quantitative measure is an important empirical
insight, as a recurring argument against the use of gender-inclusive German is
that it supposedly makes written texts too long and complicated. It is also
argued that gender-inclusive language has negative effects on language
learners. However, such effects are only likely if gender-inclusive texts are
very different from those that are not gender-inclusive. In our
corpus-linguistic study, we manually annotated German press texts to identify
the parts that would have to be changed. Our results show that, on average,
less than 1% of all tokens would be affected by gender-inclusive language. This
small proportion calls into question whether gender-inclusive German presents a
substantial barrier to understanding and learning the language, particularly
when we take into account the potential complexities of interpreting masculine
generics.
Related papers
- The Lou Dataset -- Exploring the Impact of Gender-Fair Language in German Text Classification [57.06913662622832]
Gender-fair language fosters inclusion by addressing all genders or using neutral forms.
Gender-fair language substantially impacts predictions by flipping labels, reducing certainty, and altering attention patterns.
While we offer initial insights on the effect on German text classification, the findings likely apply to other languages.
arXiv Detail & Related papers (2024-09-26T15:08:17Z) - 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) - 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) - Shifting social norms as a driving force for linguistic change:
Struggles about language and gender in the German Bundestag [0.0]
We show that language and gender has been a recurring issue in the German Bundestag since the 1980s.
We demonstrate how this is reflected in linguistic practices of the Bundestag.
We will discuss implications of these earlier language battles for the currently very heated debate about gender-inclusive language.
arXiv Detail & Related papers (2024-02-06T10:49:28Z) - Gender, names and other mysteries: Towards the ambiguous for
gender-inclusive translation [7.322734499960981]
This paper explores the case where the source sentence lacks explicit gender markers, but the target sentence contains them due to richer grammatical gender.
We find that many name-gender co-occurrences in MT data are not resolvable with 'unambiguous gender' in the source language.
We discuss potential steps toward gender-inclusive translation which accepts the ambiguity in both gender and translation.
arXiv Detail & Related papers (2023-06-07T16:21:59Z) - Measuring Gender Bias in Word Embeddings of Gendered Languages Requires
Disentangling Grammatical Gender Signals [3.0349733976070015]
We demonstrate that word embeddings learn the association between a noun and its grammatical gender in grammatically gendered languages.
We show that disentangling grammatical gender signals from word embeddings may lead to improvement in semantic machine learning tasks.
arXiv Detail & Related papers (2022-06-03T17:11:00Z) - 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) - 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) - Gender in Danger? Evaluating Speech Translation Technology on the
MuST-SHE Corpus [20.766890957411132]
Translating from languages without productive grammatical gender like English into gender-marked languages is a well-known difficulty for machines.
Can audio provide additional information to reduce gender bias?
We present the first thorough investigation of gender bias in speech translation, contributing with the release of a benchmark useful for future studies.
arXiv Detail & Related papers (2020-06-10T09:55:38Z) - 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.