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As part of the WMT-2023 "Test suites" shared task, in this paper we summarizethe results of two test suites evaluations: MuST-SHE-WMT23 and INES. Byfocusing on the en-de and de-en language pairs, we rely on these newly createdtest suites to investigate systems' ability to translate feminine and masculinegender and produce gender-inclusive translations. Furthermore we discussmetrics associated with our test suites and validate them by means of humanevaluations. Our results indicate that systems achieve reasonable andcomparable performance in correctly translating both feminine and masculinegender forms for naturalistic gender phenomena. Instead, the generation ofinclusive language forms in translation emerges as a challenging task for allthe evaluated MT models, indicating room for future improvements and researchon the topic.

Test Suites Task: Evaluation of Gender Fairness in MT with MuST-SHE and INES