Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES
원문 초록
As part of the WMT-2023 "Test suites" shared task, in this paper we summarize the results of two test suites evaluations: MuST-SHE-WMT23 and INES. By focusing on the en-de and de-en language pairs, we rely on these newly created test suites to investigate systems' ability to translate feminine and masculine gender and produce gender-inclusive translations. Furthermore we discuss metrics associated with our test suites and validate them by means of human evaluations. Our results indicate that systems achieve reasonable and comparable performance in correctly translating both feminine and masculine gender forms for naturalistic gender phenomena. Instead, the generation of inclusive language forms in translation emerges as a challenging task for all the evaluated MT models, indicating room for future improvements and research on the topic.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Fairness-Aware Grouping for Continuous Sensitive Variables: Application for Debiasing Face Analysis with respect to Skin Tone
Guiding LLM Decision-Making with Fairness Reward Models
Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
Datasets for Fairness in Language Models: An In-Depth Survey
hyperFA*IR: A hypergeometric approach to fair rankings with finite candidate pool
토론
이 논문과 연결된 포럼 글이 아직 없습니다.