Establishing Strong Baselines for the New Decade:Sequence Tagging, Syntactic and Semantic Parsing with BERT
Table 6: Test results for semantic dependency parsing in Chinese, where unlabeled and labeled dependency F1 scores (UF and LF) are used as the evaluation metrics. The standard deviations are also reported in Section A.3. NEWS: newswire, TEXT: textbook.
['[EMPTY]', '[BOLD] NEWS  [BOLD] UF', '[BOLD] NEWS  [BOLD] LF', '[BOLD] TEXT  [BOLD] UF', '[BOLD] TEXT  [BOLD] LF']
No significant difference is found between \BERT and +BERT. +BERT significantly outperforms the previous state-of-the-art by 4% and 7.5% in LF for NEWS and TEXT, which confirms that BERT embeddings are very effective for semantic dependency parsing in both English and Chinese.