R obert NLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for E nglish (2020)

Abstract: This paper presents our system at the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. Using a biaffine classifier architecture (Dozat and Manning, 2017) which operates directly on finetuned RoBERTa embeddings, our parser generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens in the sentence. We address label sparsity issues by replacing lexical items in relations with placeholders at prediction time, later retrieving them from the parse in a rule-based fashion. In addition, we ensure structural graph constraints using a simple set of heuristics. On the English blind test data, our system achieves a very high parsing accuracy, ranking 1st out of 10 with an ELAS F1 score of 88.94%.

## introduction enhanced universal dependencies are an extension of the widely used universal dependencies (ud) framework for syntactic dependency annotation @xcite . designed with shallow natural language understanding tasks in mind, enhanced ud extends basic ud trees by including a number of additional dependencies between tokens in order to make relations between content words more explicit, especially in the presence of linguistic phenomena such as coordination, raising/control, and relative clauses @xcite . while there is evidence for the utility of enhanced dependencies in downstream applications @xcite , adding these relations means that dependency structures are not generally constrained to trees any more, which makes parsing them a different problem with its own set of challenges. research on ud parsing has so far mostly focused on producing syntax trees according to the basic ud specification (e.g., in the conll 2017 and 2018 shared tasks). prior work on inducing enhanced ud graphs @xcite @xcite infers enhanced ud representations by first parsing text into basic ud trees and then adding enhanced relations by applying rule-based or machine-learning modules. this approach has the disadvantage of propagating errors in the basic layer to the enhanced parse. for our submission to the iwpt 2020 shared task @xcite , we follow an alternative approach. we do not distinguish between the basic ud tree and the enhanced part of the graph, instead treating all types of dependencies equally and extracting them jointly. following the approach of @xcite , we use a biaffine classifier architecture in which we predict the most likely dependency label (or absence of a dependency) for each pair of tokens in the sentence, forming a dependency graph from the union of these predictions. similar to @xcite , we extract the inputs for the biaffine classifier directly from fine-tuned contextualized word embeddings, roberta @xcite in our case, using a scalar mixture of hidden layers @xcite . we overcome the problem of sparsity issues caused by enhanced ud's large lexicalized label set by replacing lexical items with placeholders at prediction time and later retrieving them from the full parse via a set of rules. surprisingly, this simple approach, combined with a straightforward heuristic ensuring that each node receives a head, results in valid enhanced ud graphs for 99% of all sentences in the english blind test data. despite being conceptually simple and easy to implement, our system sets a new state of the art for enhanced ud parsing for english, scoring first out of ten submissions on the blind test data according to the official elas evaluation metric. while our system is currently available only for english, adapting it to most other languages should be feasible with relatively little effort. ## our model this section describes the components of our parser as submitted to the shared task. ## experiments this section describes our submission to the shared task, as well as a number of additional experiments we conducted to contextualize our results. ## discussion and conclusion with our submission to the iwpt 2020 shared task, we have demonstrated a conceptually simple, yet highly effective method for parsing enhanced universal dependencies from english text. although we have focused on english in our submission, we believe that our system should in principle be easily adaptable to other languages as the only language-specific part of out model is its handling of lexicalized relations. while certain other languages (e. g., czech or estonian) have more complex label inventories including for example case information as well, this should not pose a problem for our delexicalization strategy. however, the adaptation might require a moderate amount of manual work, and it remains to be seen how effective the lexicalization strategy is for other languages, a question that may be addressed in future work.