Cross-lingual Embeddings Reveal Universal and Lineage-Specific Patterns in Grammatical Gender Assignment (2020)

Abstract: Grammatical gender is assigned to nouns differently in different languages. Are all factors that influence gender assignment idiosyncratic to languages or are there any that are universal? Using cross-lingual aligned word embeddings, we perform two experiments to address these questions about language typology and human cognition. In both experiments, we predict the gender of nouns in language X using a classifier trained on the nouns of language Y, and take the classifier’s accuracy as a measure of transferability of gender systems. First, we show that for 22 Indo-European languages the transferability decreases as the phylogenetic distance increases. This correlation supports the claim that some gender assignment factors are idiosyncratic, and as the languages diverge, the proportion of shared inherited idiosyncrasies diminishes. Second, we show that when the classifier is trained on two Afro-Asiatic languages and tested on the same 22 Indo-European languages (or vice versa), its performance is still significantly above the chance baseline, thus showing that universal factors exist and, moreover, can be captured by word embeddings. When the classifier is tested across families and on inanimate nouns only, the performance is still above baseline, indicating that the universal factors are not limited to biological sex.

## experimental materials and settings in this section, we present the languages involved in this study along with the source of our data. then, we provide an overview of the cross-lingual word embedding method and the settings of the classifier used for gender transfer. ## gender transfer at the language level in this section, we analyse the results of the experiment from two different perspectives. first, we consider the broad transferability of gender across languages by measuring the accuracy across all possible pairs of languages in the dataset (section 3.1, figures 1 and 3). while this step provides an overview of the accuracy of gender transfer, it is also extremely influenced by the different gender systems across languages. for instance, asking a language that has the masculine/feminine system to predict the categories on a language that has a masculine/feminine/neuter system is by definition going to result in a low accuracy since the source language does not have information about neuter nouns. to overcome this issue, we perform an additional analysis with narrower scope, where we compare pairs of only those languages that have isomorphic systems (section 3.2). as an example, we use languages that have masculine/feminine to predict the gender in languages that also have masculine/feminine. the rationale behind narrowing the scope is that it enables us to focus on the question of how similar the distributions of nouns across gender classes are, abstracting away from possible differences between the number of classes and their types. we define a random guessing baseline for the transfer between each pair of languages. the baseline is the accuracy that would have been achieved by a classifier that makes a random guess based solely on gender probabilities in the source language. the accuracy it would achieve is ## conclusion this study investigates how grammatical gender is transferable across languages from a transfer learning point of view. the cross-lingual word embeddings are considered as the source of knowledge shared between languages from which the grammatical gender of nouns are predicted using a multilayer perceptron. the empirical results reveals that there exist some universal and lineage-specific patterns in the grammatical gender assignment. first, our analysis of gender transfer between afro-asiatic and indo-european languages indicated that partly successful gender transfer is possible between non-related languages. this observation supports the existence of universal factors in gender assignment. the accuracy of the classifier is higher than the random baseline even when it is tested on inanimate nouns only, which means that the universal factors are not limited to biological sex. second, our analysis of gender transfer between indo-european languages demonstrates that the phylogenetic distance between languages has a negative effect on the success of the transfer, which suggests that some factors of gender assignment are not universal. these results match with the literature by showing that gender assignment is a mixture of universal and idiosyncratic factors. third, we also found that gender transfer does not work in the same way for all nouns. the prediction accuracy is significantly lower for inanimate nouns than for animate nouns and for inflected forms than for citation forms. this effect is found when considering both family-internal and familyexternal transfers, which provides further evidence in favor of the universal factors in gender assignment. we would like to make a few caveats and suggestions about the future development of the current study. while we address the universality of gender assignment cross-linguistically, our data is restricted to languages from two families and our word embeddings are trained on data from specific domains. additional data from a more diverse sample is needed to further confirm our observations. furthermore, we cannot fully exclude that the observed similarities are an areal effect caused by contact. it should also be noted that we cannot identify which universal factors enable the classifier to perform above the baseline. a more fine-grained word-level analysis would be required to find the possible contributors to this. linguistically, grammatical gender is strongly tied to the semantic and formal properties of nouns. since the cross-lingual word embeddings used in this study encode both the formal and semantic information, we cannot disentangle the relative contributions of form and semantics to gender transfer. finally, it should be mentioned that an important line of research in modern nlp focuses on gender bias present in naturally occurring texts @xcite . the combination of these questions and approaches with our perspective might become an interesting research direction. supplementary materials, including raw data and scripts for analysis are openly available. 5