Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Table 4: Ablation test on the PTB dataset. “w/o top LSTM” is the full model without the top LSTM layer. “w Char LSTM” is the full model with the extra Character-level LSTM layer. “w. embedding” stands for the full model using the pretrained word embeddings. “w. MSE loss” stands for the full model trained with MSE loss.
['Model', 'LP', 'LR', 'F1']
We perform an ablation study by removing/adding components from a our model, and re-train the ablated version from scratch. This gives an idea of the relative contributions of each of the components in the model. It seems that the top LSTM layer has a relatively big impact on performance. This may give additional capacity to the model for capturing long-term dependencies useful for label prediction. We used an extra 1-layer character-level BiLSTM to compute an extra word level embedding vector as input of our model. It’s seems that character-level features give marginal improvements in our model. Unsurprisingly, the model trained with MSE loss underperforms considerably a model trained with the rank loss.