RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English
Stefan Grünewald, Annemarie Friedrich · 2020
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 fine-tuned 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 1 st out of 10 with an ELAS F1 score of 88.94 %.