Handling gender across languages remains a persistent challenge for Machine Translation (MT) and Large Language Models (LLMs), especially when translating from gender-neutral languages into morphologically gendered ones, such as English to Italian. English largely omits grammatical gender, while Italian requires explicit agreement across multiple grammatical categories. This asymmetry often leads MT systems to default to masculine forms, reinforcing bias and reducing translation accuracy. To address this issue, we present the Contextual Gender Annotation (ConGA) framework, a linguistically grounded set of guidelines for word-level gender annotation. The scheme distinguishes between semantic gender in English through three tags, Masculine (M), Feminine (F), and Ambiguous (A), and grammatical gender realisation in Italian (Masculine (M), Feminine (F)), combined with entity-level identifiers for cross-sentence tracking. We apply ConGA to the gENder-IT dataset, creating a gold-standard resource for evaluating gender bias in translation. Our results reveal systematic masculine overuse and inconsistent feminine realisation, highlighting persistent limitations of current MT systems. By combining fine-grained linguistic annotation with quantitative evaluation, this work offers both a methodology and a benchmark for building more gender-aware and multilingual NLP systems.

The assessment of gender bias in Machine Translation critically depends on reliable methods for identifying grammatical gender in system outputs. In this paper, we compare the automated WinoMT annotation pipeline, based on word alignment and morphological tagging, with an instruction-tuned LLM (Qwen3-8B) used to annotate grammatical gender in English–Italian translations. Both approaches achieve similar levels of agreement with a human-annotated gold standard, but exhibit distinct systematic weaknesses. The WinoMT pipeline is sensitive to alignment shifts and morphological tagging limitations, often resulting in indeterminate gender labels. The LLM, in contrast, tends to favour binary gender labels even when noun phrases are morphologically gender-invariant. A qualitative analysis of model outputs and reasoning traces further suggests a lack of stable generalization of grammatical gender rules, even when illustrative exemplars are provided for in-context learning. This highlights important methodological limitations in using LLMs as objective annotators for gender evaluation tasks.