Graph Rewriting for Enhanced U niversal D ependencies (2021)

Abstract: This paper describes a system proposed for the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (EUD). We propose a Graph Rewriting based system for computing Enhanced Universal Dependencies, given the Basic Universal Dependencies (UD).

## introduction the iwpt 2021 shared task on parsing into enhanced universal dependencies @xcite is a second edition of an equivalent shared task in 2020 @xcite . the goal of the shared task is to produce eud @xcite , with several new annotation layers expressed on top of ud annotations @xcite . in the previous shared task, there were two kinds of approaches: producing eud annotation from raw text with machine learning methods or producing eud from ud with a rule-based approach (with or without some learning to optimize rule usage). like (heinecke, 2020) or @xcite , our proposal corresponds to the second approach: we used an existing tool for producing ud annotations and work only on the conversion from ud to eud. unlike other rule-based approaches, we used grew, a generic graph rewriting tool @xcite , in order to describe the rules for enhancement. another specificity of our work is that we primarily design our rules by following the guidelines. even if, in a secondary step and in the context of the shared task, we adapt the system to the corpora which diverge from the guidelines (section 2.6), we can easily provide a system closer to the guidelines, adpatable to languages specificities. our system achieved 81.58 elas score on the task, starting from udpipe annotation with an las of 85.77. in the paper, we present the system, analyse the results and make some in-depth analysis on french and english of discrepancies between gold eu d data and the output of our system starting gold u d annotations. ## results data we submit to the task, called grew(udpipe), is the output of the application of a language spe- in addition to the results provided by the orgnanisers, we make complementary analysis, focusing on the ud to eud transformation. in ## analysis of discrepancies with the gold annotation in french and english in order to better understand the behaviour of our grs, we made some manual inspection of the difference between the annotation grew(gold u d ) and gold eu d . we focused on the two languages for which we had the grammatical skills to analyze the discrepancies: french and english. ## conclusion we have observed that many conversion problem arise with the casedeprel layer. this layer is of course highly dependent of the language (because lexical information is used in relation definitions). this prevent the new relation to be universal and we believe that this is counterproductive in the objective of a universal description among a large set of languages. in this paper, we have proposed a rule-based system for computing eud annotation from ud. our raw results are far behind the best systems of the task. this can be explained by the fact that we are dependent of the basic ud annotation provided by another tool. moreover, the manual inspection we have made shows that, at least on english and french, the gold test data used in the task are not error-free and contains several annotations that are not described in the guidelines. we can suspect that this is in favour of the learning based approaches which are designed to adapt to the annotated data, completely ignoring the guidelines. despite its weakness, we believe that our system have several benefits: