Investigating Dominant Word Order on U niversal D ependencies with Graph Rewriting (2021)

Abstract: This paper details experiments we performed on the Universal Dependencies 2.7 corpora in order to investigate the dominant word order in the available languages. For this purpose, we used a graph rewriting tool, GREW, which allowed us to go beyond the surface annotations and identify the implicit subjects. We first measured the distribution of the six different word orders (SVO, SOV, VSO, VOS, OVS, OSV) in the corpora and investigated when there was a significant difference in the corpora within a given language. Then, we compared the obtained results with information provided in the WALS database (Dryer and Haspelmath, 2013) and in ( ̈Ostling, 2015). Finally, we examined the impact of using a graph rewriting tool for this task. The tools and resources used for this research are all freely available.

## introduction language typology has proven to be useful in natural language processing (nlp) @xcite , for example for improving performance in language transfer @xcite and joint learning. as noted by @xcite "wals is currently by far the most commonly-used typological resource in nlp due to its broad coverage of features and languages". however, the wals database @xcite has been compiled from the work of 55 linguists 1 and is not systemically based on a large quantity of data. moreover, it does not provide all the considered features for all the languages it covers. on the other hand, the universal dependencies (ud) framework @xcite provides a large number of corpora annotated in dependency syntax (in version 2.7, there are 183 corpora for 104 languages). we decided to automatically extract from the ud corpora one of the most used features in nlp, the dominant word order, i.e. the way the subject (s), verb (v) and object (o) are ordered in a language (feature 81a in wals). to do so, we use a freely available graph rewriting tool, which allows us to perform complex searches, to take into account the context of the construction and to add or modify the existing annotations to expose relations which are not directly accessible in the corpora. these experiments led us to define what is a dominant word order, to observe the distribution in word orders within the corpora of a given language, to determine the frequency of the different word orders in all the considered corpora, and to compare the obtained results with those of existing databases, including wals. ## determining dominant word order in multi-corpora languages we detail here the results obtained for multicorpora languages. for the mono-corpus languages, we examine our results as compared to wals' and @xcite in section 6. ## comparison with other sources amongst the 74 languages available in ud 2.7 1k , wals does not cover the seven dead languages, nor the two code-switching "languages". in addition, wals does not provide feature 81a for six languages. in @xcite , 22 languages are not in the database and seven are in neither sources, galician, hindi-english, turkish-german and four dead languages (old french, old russian, sanskrit, akkadian). foot_10 6.1 differences with wals language ud 2. we compare our results with feature 81a (order of subject, object and verb) in wals foot_11 . we have 59 languages in common and we consistently observe the same dominant word order for 48 of these. in belarusian and greek can be considered relatively free word order languages, hence the ndo order in wals. in our results, belarusian is svo with a ratio of 10.43, however the belarusian-hse corpus is based on texts included in the belarusian-russian parallel subcorpus of the russian national corpus. russian being a svo language, this may explain the high proportion of svo. moreover, it is more common to find the svo order as the basic order in written belarusian. similarly, the basic order in greek being svo, this may explain the ratio of 7.31 of svo order in our results. as for mbya guarani and urdu, the most frequent order corresponds to the order in wals. mbya guarani is ndo (svo/sov) with a ratio of 1.25 and urdu ndo (sov/svo) with a ratio of 1.52. finally, amharic has an ovs order as the most frequent order, contrary to wals'. there are six languages present in wals which do not have the feature 81a: galician, faroese, kazakh, maltese, naija and slovak. our results could therefore be used to enrich wals' data. the data presented in @xcite is computed from the automatically aligned new testament. the corpora are homogeneous and the data on which the dominant order is computed can sometimes be very small (for hungarian, 127 structures vs 876 in our experiment). moreover, östling (2015) considers the single most prevalent order as the dominant one, without taking into account the difference with the second one. we have 52 languages in common and observe the same dominant order for 38 of these. ## influence of implicit subjects as said earlier, we decided to enrich the basic ud annotations, but the choice we made is quite arbitrary. in order to evaluate how this may have impacted our observations, we conducted the same experiment without taking into account the implicit subjects. ## conclusions and perspectives the main outcome of these experiments is the determination of the dominant word order for 74 languages, based on large amounts of annotated data. this result can be used for nlp applications. on the linguistic side, our findings could be used to reinforce the results published in wals and complete them in some cases. however, our results differ from wals' for 11 languages, and for these, a more thorough analysis should be conducted by specialists of said languages. we are planning to experiment using graph rewriting to explore other universals, like @xcite or other missing features in wals. graph rewriting can be used to enrich the ud annotations but it can also be used to reorganise more deeply the tree dependency graph. in @xcite , the observations were done on such a deeper reorganisation of the dependency tree structure, proposed in surface syntactic universal de-pendency @xcite which was already produced using grew-based graph rewriting. our experiments can be replicated and extended: all the tools and resources are freely available and we also provide the patterns and scripts to be used 12 . corpora first rank without isubj first rank with isubj diff welsh-ccg vso (80.6%) vso (71.1%) -9.5% latin-llct osv (32.0%) sov (42.7%) +10.7% akkadian-riao sov (56.6%) sov (66.3%) +9.7% icelandic-icepahc svo (57.0%) svo (63.4%) +6.4% wolof-wtb svo (59.3%) svo (64.8%) +5.5% old church slavonic-proiel svo (52.0%) svo (57.4%) +5.5% czech-fictree svo (47.6%) svo (52.7%) +5.1%