How much meaning influences gender assignment across languages is an active
area of research in modern linguistics and cognitive science. We can view
current approaches as aiming to determine where gender assignment falls on a
spectrum, from being fully arbitrarily determined to being largely semantically
determined. For the latter case, there is a formulation of the neo-Whorfian
hypothesis, which claims that even inanimate noun gender influences how people
conceive of and talk about objects (using the choice of adjective used to
modify inanimate nouns as a proxy for meaning). We offer a novel, causal
graphical model that jointly represents the interactions between a noun's
grammatical gender, its meaning, and adjective choice. In accordance with past
results, we find a relationship between the gender of nouns and the adjectives
which modify them. However, when we control for the meaning of the noun, we
find that grammatical gender has a near-zero effect on adjective choice,
thereby calling the neo-Whorfian hypothesis into question.

语义对于语言中的性别确定有多大影响是现代语言学和认知科学中一个研究热点领域。本研究提出了一个新的因果图模型，共同表示名词的语法性别、意义和形容词选择之间的相互作用。研究结果表明，虽然名词的性别会影响修饰它们的形容词，但在控制名词的意义后，语法性别对形容词选择几乎没有影响，从而质疑了新沃尔夫假说。

语法性别对分布语义的影响：因果视角

Grammatical Gender's Influence on Distributional Semantics: A Causal  Perspective

When translating from notional gender languages (e.g., English) into
grammatical gender languages (e.g., Italian), the generated translation
requires explicit gender assignments for various words, including those
referring to the speaker. When the source sentence does not convey the
speaker's gender, speech translation (ST) models either rely on the
possibly-misleading vocal traits of the speaker or default to the masculine
gender, the most frequent in existing training corpora. To avoid such biased
and not inclusive behaviors, the gender assignment of speaker-related
expressions should be guided by externally-provided metadata about the
speaker's gender. While previous work has shown that the most effective
solution is represented by separate, dedicated gender-specific models, the goal
of this paper is to achieve the same results by integrating the speaker's
gender metadata into a single "multi-gender" neural ST model, easier to
maintain. Our experiments demonstrate that a single multi-gender model
outperforms gender-specialized ones when trained from scratch (with gender
accuracy gains up to 12.9 for feminine forms), while fine-tuning from existing
ST models does not lead to competitive results.

通过将说话者的性别元数据合并到单个 “多性别” 神经语音转换模型中，可以避免性别偏见并提高性别准确性（女性形式可提高 12.9），相比专门的性别模型，该模型训练自零效果更好，而基于现有频培训模型的微调则不具有竞争力。