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Extending Challenge Sets to Uncover Gender Bias in Machine Translation:
  Impact of Stereotypical Verbs and Adjectives

Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives

24 July 2021
Jonas-Dario Troles
Ute Schmid
ArXivPDFHTML

Papers citing "Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives"

12 / 12 papers shown
Title
MT-LENS: An all-in-one Toolkit for Better Machine Translation Evaluation
MT-LENS: An all-in-one Toolkit for Better Machine Translation Evaluation
Javier García Gilabert
Carlos Escolano
Audrey Mash
Xixian Liao
Maite Melero
AIMat
ELM
84
0
0
16 Dec 2024
What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine
  Translation with a Human-centered Study
What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study
Beatrice Savoldi
Sara Papi
Matteo Negri
Ana Guerberof
L. Bentivogli
47
7
0
01 Oct 2024
Are Female Carpenters like Blue Bananas? A Corpus Investigation of
  Occupation Gender Typicality
Are Female Carpenters like Blue Bananas? A Corpus Investigation of Occupation Gender Typicality
Da Ju
Karen Ulrich
Adina Williams
35
2
0
06 Aug 2024
Does Context Help Mitigate Gender Bias in Neural Machine Translation?
Does Context Help Mitigate Gender Bias in Neural Machine Translation?
Harritxu Gete
Thierry Etchegoyhen
17
1
0
18 Jun 2024
Whose wife is it anyway? Assessing bias against same-gender
  relationships in machine translation
Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation
Ian Stewart
Rada Mihalcea
27
4
0
10 Jan 2024
Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine
  Translation and Language Modeling
Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine Translation and Language Modeling
Matúš Pikuliak
Andrea Hrckova
Stefan Oresko
Marian Simko
37
6
0
30 Nov 2023
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for
  Fairer Instruction-Tuned Machine Translation
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation
Giuseppe Attanasio
Flor Miriam Plaza del Arco
Debora Nozza
Anne Lauscher
23
18
0
18 Oct 2023
What about em? How Commercial Machine Translation Fails to Handle
  (Neo-)Pronouns
What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns
Anne Lauscher
Debora Nozza
Archie Crowley
E. Miltersen
Dirk Hovy
26
21
0
25 May 2023
Angler: Helping Machine Translation Practitioners Prioritize Model
  Improvements
Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
Samantha Robertson
Zijie J. Wang
Dominik Moritz
Mary Beth Kery
Fred Hohman
38
15
0
12 Apr 2023
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating
  Gender Accuracy in Machine Translation
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
Anna Currey
Maria Nadejde
R. Pappagari
Mia C. Mayer
Stanislas Lauly
Xing Niu
B. Hsu
Georgiana Dinu
28
32
0
02 Nov 2022
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias
  in Speech Translation
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation
Beatrice Savoldi
Marco Gaido
L. Bentivogli
Matteo Negri
Marco Turchi
38
26
0
18 Mar 2022
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
335
4,230
0
23 Aug 2019
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