Parsing as Pretraining
Table 4: Comparison against related work on the en-ewt ud test set. ⋄ are other sequence tagging parsers.
['[BOLD] Models', 'en-ewt las', 'en-ewt uas']
Tables In this context, it is worth remarking that all our models only use words as features. For constituents, BERT obtains a 93.5% F1-score vs the 95.8% reported by \citeauthorkitaev2018BERT \shortcitekitaev2018BERT and \citeauthorzhou-zhao-2019-head \shortcitezhou-zhao-2019-head, which are the current best performing models and use BERT representations as input (together with PoS tag embedddings, and in the case of \citeauthorzhou-zhao-2019-head also char embeddings). Against other sequence tagging constituent parsers, we improve by more than two points the 91.2% by \citeauthorVilaresMTL2019 \shortciteVilaresMTL2019, which was the current state of the art for this particular paradigm, and used task-specific decoders and multi-task learning, together with PoS tags and character embeddings. For dependencies, we observe similar tendencies.