Universal Dependency Parsing from Scratch
Table 3: Ablation results for the tokenizer. All metrics in the table are macro-averaged dev F1.
['System', 'Tokens', 'Sentences', 'Words']
We perform ablation studies on the less standard components in the tokenizer, namely the gating mechanism in Eq. (gating), the convolutional residual connections (conv), and the seq2seq model in the MWT expander (seq2seq), on all 61 big treebanks. When taking a closer look, we find that both gating and conv show a mixed contribution to each treebank, and we could have improved overall performance further through treebank-level component selection. One surprising discovery is that conv greatly helps identify MWTs in Hebrew (+34.89 Words F1) and sentence breaks in Ancient Greek-PROIEL (+18.77 Sents F1). Finally, we note that dropout plays an important role in safeguarding the tokenizer from overfitting.