Multilingual Gradient Word-Order Typology from U niversal D ependencies (2024)

Abstract: While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite. Existing typological databases, including WALS and Grambank, suffer from inconsistencies primarily caused by their categorical format. Furthermore, typological categorisations by definition differ significantly from the continuous nature of phenomena, as found in natural language corpora. In this paper, we introduce a new seed dataset made up of continuous-valued data, rather than categorical data, that can better reflect the variability of language. While this initial dataset focuses on word-order typology, we also present the methodology used to create the dataset, which can be easily adapted to generate data for a broader set of features and languages.

## introduction data from the field of linguistic typology has the potential to be useful in training nlp models @xcite . however, the main existing typological databases, wals (world atlas of language structures) @xcite and grambank @xcite , contain inconsistent and contradictory information @xcite . these issues stem, in large part, from the categorical format of the data, which is over-simplistic and therefore cannot capture the nuance and variability that exist in natural language. for example, one of the features describes the ordering of adjectives and the noun they modify. the categories in these datasets are noun-adjective, adjective-noun, or variable. limiting the options to these three categories removes any information differentiating a language that employs noun-adjective ordering 10% of the time from one that does so 90% of the time. in addition, the threshold between the noun-adjective and * these authors contributed equally to this work. adjective-noun categories and the variable category is often not clear, which can lead to inconsistencies in the data. as an example, the same 90% noun-adjective language might be classified as variable in one database, but might be seen as consistently noun-adjective enough to be classified in the noun-adjective category in another database. in this paper, we apply recommendations presented in @xcite and extend their analysis by introducing a new continuous-valued typological dataset that removes the need to oversimplify data into categories. in particular, we focus on word-level typology, and present a method for extracting gradient typology that utilizes the part of speech annotations available in the universal dependencies (ud) treebank corpus @xcite . we then propose a novel regression-based typology task. this new dataset and the methods used to create it are beneficial not only to nlp, but also potentially ## continuous-valued seed dataset we introduce a seed dataset based on the idea of continuous representations of linguistic features @xcite . this dataset is currently small, with coverage of fewer than 100 languages across a limited number of features. however, the process used to create it, described in section 3.1, can be easily adapted for broader feature coverage, as well as broader language coverage. ## proposed task and model comparison because categorical typological datasets are a core part of many existing typology-related nlp tasks, these tasks also suffer from many of the problems that the underlying datasets do. examples of these tasks include typological feature prediction @xcite @xcite , low-resource language vocabulary prediction @xcite , and language identification from speech @xcite . it is for this reason that we introduce, along with the seed dataset, a new task predicting these novel continuous typological features. unlike previous typological prediction tasks, the one we present here is regression-based. ## conclusion information from the field of linguistic typology has the potential to benefit the field of nlp. unfortunately, the data from existing typological databases has been unreliable, largely due to their reliance on categorical features and those features' inability to represent the variability found in natural language. in this paper, we attempt to address this problem by introducing a new continuous-valued seed dataset, and argue that it is indeed better able to reflect the nuance of natural language when it comes to word order. in addition, we provide our dataset creation methodology that can be easily adapted in the future to generate data for a wider array of languages and features. finally, we present a novel regression task based on predicting the feature values of this new dataset.