R obert NLP at the IWPT 2021 Shared Task: Simple Enhanced UD Parsing for 17 Languages (2021)

Abstract: This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97.

## introduction enhanced universal dependencies @xcite are an extension of the widely used universal dependencies (ud) framework for syntactic dependency annotation @xcite . to better model linguistic phenomena such as coordination, raising/control, and relative clauses, enhanced ud extends basic ud trees by including additional dependencies between tokens in order to make relations between content words more explicit. while there is evidence for the utility of enhanced dependencies in downstream applications @xcite , adding them means that dependency structures are not constrained to trees any more, which makes parsing them a different problem with its own set of challenges. in the past, research on enhanced ud parsing has mostly focused on rule-based methods for extracting enhanced graphs from existing basic trees @xcite ; full results can be found at https://universaldependencies.org/ iwpt21/results.html . @xcite . furthermore, only a relatively small number of ud treebanks is annotated with enhanced dependencies. recently, however, interest in enhanced ud has increased, most notably with the iwpt 2020 shared task @xcite , which asked contestants to produce enhanced ud graphs from raw text for 17 languages. for our submission to the 2021 edition of the shared task @xcite , we adapt our english-only submission from last year @xcite to all 17 languages that are part of the competition. the core principles of our system remain the same: instead of a strictly rule-based system as used by @xcite , we use a hybrid rule-based/machine-learning system to retrieve lexical material for dependency labels at prediction time (see sec. 2.5). in order to further increase our parser's accuracy as well as its robustness across treebanks, we use model ensembling. as shown in ## our model this section describes the components of our parser as submitted to the shared task. ## experiments this section describes our main submission, as well as a number of additional experiments. ## conclusion in this paper, we have described our submission to the iwpt 2021 shared task, which ranked 3 rd out of 9 with an average elas f1 score of 86.97. our model is an extension of the previously english-only system @xcite , demonstrating that the same approach is also yields very good results for other languages with only relatively minor modifications. in postsubmission ablation experiments, we find that our parser benefits from model ensembling and a machine learning-assisted approach to label lexicalization. a remaining issue of our parser is its rather poor performance in a low-resource setting (tamil). addressing this weakness, ideally while maintaining the parser's relatively simple core architecture, may be a promising avenue for future work.