AttributeError: 'LeafParseNode' object has no attribute 'oracle_label' about self-attentive-parser HOT 3 CLOSED

This is a known limitation with how the parser is currently formulated: each tree must contain a non-terminal node at the root. In your example above, the "part-of-speech" tag PU is the root node, which breaks that assumption.

The way I usually work around this is by wrapping all trees with a "TOP" symbol for the root node, and then disabling the "strip_top" flag when the treebank is known to contain examples that don't have a root non-terminal label.

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It looks like you're using the English ELMo embeddings to learn a parser for Chinese, which works poorly due to the language mismatch. I recommend that you use the Chinese version of BERT instead. Here is the command needed for training the model: https://github.com/nikitakit/self-attentive-parser/blob/master/EXPERIMENTS.md#chinese-models

It also looks like our dataset sizes don't match. See here for CTB data preparation scripts. My data sizes are 17544 train / 352 dev / 348 test.

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This is a known limitation with how the parser is currently formulated: each tree must contain a non-terminal node at the root. In your example above, the "part-of-speech" tag PU is the root node, which breaks that assumption.

The way I usually work around this is by wrapping all trees with a "TOP" symbol for the root node, and then disabling the "strip_top" flag when the treebank is known to contain examples that don't have a root non-terminal label.

Great! Thank you very much! That is very helpful.
However, When I tried to train the model on CTB data as the following comand:

python src/main.py train --use-elmo --model-path-base models/ctb_elmo --num-layers 4 --epochs 500

I got a result:

epoch 134 batch 112/112 processed 2,985,118 batch-loss 6.4597 grad-norm 17.0195 epoch-elapsed 0h05m52s total-elapsed 13h54m52s  
dev-fscore (Recall=84.26, Precision=85.77, FScore=85.01, CompleteMatch=19.32) dev-elapsed 0h00m15s total-elapsed 13h55m07s  
Terminating due to lack of improvement in dev fscore.

and this score is less than your "91.69 F1 on CTB 5.1 test set in benepar_zh"
my CTB data size:

• train data: 22277
• dev data: 1761

can you provide parameter settings in detail?

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