Investigating C ross- L inguistic A djective O rdering T endencies with a L atent- V ariable M odel (2020)

Abstract: Across languages, multiple consecutive adjectives modifying a noun (e.g. “the big red dog”) follow certain unmarked ordering rules. While explanatory accounts have been put forward, much of the work done in this area has relied primarily on the intuitive judgment of native speakers, rather than on corpus data. We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent-variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different. We utilize this novel statistical model to provide strong converging evidence for the existence of universal, cross-linguistic, hierarchical adjective ordering tendencies.

## introduction most native speakers of a language would agree that certain adjective orderings are preferable to others. for instance, in english, "the big red dog" sounds natural while "the red big dog" sounds very awkward. similar ordering preferences have been found to apply universally across the languages in the world: for example, the adjective for "big" in most languages tends to be farther away from the noun, syntactically, than "red." for an overview of these phenomena, see @xcite . there are many explanatory accounts of crosslinguistic adjective ordering in the linguistics literature, the most popular being hierarchical tendencies based on semantic categories of adjectives @xcite @xcite . for instance, @xcite and @xcite note that adjectives describing size tend to be placed further from the noun than those describing color in most languages. however, most of these studies have relied primarily on the judgment of native speakers rather than on corpus data, and those corpus-based models that do exist have focused exclusively on english @xcite @xcite @xcite @xcite @xcite . in this paper, we make use of tools and techniques from statistical modeling to provide strong converging evidence supporting a hierarchical theory of cross-linguistic adjective ordering. specifically, we present a novel interpretable, multi-lingual, latent-variable model of adjective ordering that directly enforces a hierarchy of semantic classes and is trained entirely using corpus data. we empirically show that our model accurately orders adjectives across 24 different languages, even when tested on languages that it has not been trained on. in doing so, we demonstrate the existence of universal, cross-linguistic, hierarchical tendencies in adjective ordering. ## adjective ordering consider the following english phrases, taken from @xcite : here, (3) presents the most natural ordering of "brown" and "chinese" (to illustrate this, substitute "bear" with "house"), but (4) is also correct because a "brown bear" is an adjective-noun collocate. for a more detailed discussion on adjective ordering exceptions, see @xcite . ## a latent-variable model a natural mathematical formalization of adjective ordering is as a latent-variable model. a latentvariable model relates a set of observable variables to a set of unobservable (latent) ones. here, we observe how adjectives are ordered in corpus data and from this infer an ordered set of latent adjective classes. this allows us to determine the ordering of an arbitrary set of adjectives by referencing their class memberships and the class order. like other latent-variable models, such as latent semantic analysis @xcite and latent dirichlet allocation @xcite , our model aims to fit the data using a lower-dimensional space. in particular, the number of adjective classes is much smaller than the size of the vocabulary or the size of the pre-trained adjective embeddings. ## data this section describes our english, multi-lingual, and additional languages datasets. ## experimental details and hyperparameters we for all models. we report the exact expectation of the random baseline. all significance testing is done with permutation tests following @xcite , using 10,000 random permutations and significance at α = 0.05. all differences between model performance and the corresponding random baselines are significant with @xmath0.01. ## english experiments our english experiments serve to demonstrate the basic correctness of the model. we also provide a qualitative analysis of ef. ## transfer learning experiments an important claim of the hierarchical theory for adjective ordering is that the hierarchy applies universally across languages. if this is the case, then we should be able to accurately order adjectives from languages that we have not trained on. ## conclusion in summary, our work presents converging evidence that adjectives exhibit universal hierarchical ordering tendencies, with the added observations that individual languages feature additional unique tendencies not shared by others, and that adjective ordering is symmetric with respect to the noun.