Extracting Headless MWEs from Dependency Parse Trees:Parsing, Tagging, and Joint Modeling Approaches
Table 3: Flat-structure identification test-set F1 scores (%) with bi-LSTM (top) and BERT (bottom). The cell with the best result for each treebank has blue shading; results within one standard deviation of the best are bolded.
['[ITALIC] w/ bi-LSTM Treebank', '[ITALIC] w/ bi-LSTM Treebank', 'Compl. Ratio ↓', 'Parsing', 'Tagging', 'MTL Parsing', 'MTL Tagging', 'Joint Decoding']
S4SS0SSS0Px3 Results We report F1 scores based on multi-word headless-structure extraction. Tagging is consistently better than parsing except for two treebanks with BERT feature extractor. Tagging beats parsing in all but two combinations of treebank and feature extractor. As hypothesized, our joint decoder improves over both strategies by 0.69% (1.64%) absolute through combined decisions from parsing and tagging with(out) BERT. We also compare the joint decoding setting with MTL training strategy alone. While joint decoding yields superior F1 scores, MTL is responsible for a large portion of the gains: it accounts for over half of the average gains with bi-LSTMs, and when we use pre-trained BERT feature extractors, the accuracies of jointly-trained taggers are essentially as good as joint decoding models.