Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing – A Tale of Two Parsers Revisited We gratefully acknowledge the inspiration for our subtitle in the seminal paper by \newcitezhang08emnlp.
Table 2: Labeled attachment score on 13 languages for parsing models with and without deep contextualized word representations.
['[BOLD] Language', 'tr', 'gr', 'tr+E', 'gr+E', 'tr+B', 'gr+B']
The results clearly corroborate our main hypothesis. While ELMo and BERT provide significant improvements for both transition-based and graph-based parsers, the magnitude of the improvement is greater in the transition-based case: 3.99 vs. 2.85 for ELMo and 4.47 vs. 3.13 for BERT. In terms of error reduction, this corresponds to 21.1% vs. 16.5% for ELMo and 22.5% vs. 17.4% for BERT. The differences in error reduction are statistically significant at α=0.01 (Wilcoxon).