Universal Dependencies Parsing for Colloquial Singaporean English
Table 3: Comparison of the scale of sources for training word embeddings
['[EMPTY]', 'Sentences', 'Words', 'Vocabulary']
When using the neural stacking model, we fix the model configuration for the base English parser model and choose the size of the hidden vector and the number of bi-LSTM layers stacked on top based on the performance on the development set. It turns out that a 1-layer bi-LSTM with 900 hidden dimension performs the best, where the bigger hidden layer accommodates the elongated input vector to the stacked bi-LSTM and the fewer number of recurrent layers avoids over-fitting on the small Singlish dependency treebank, given the deep bi-LSTM English parser network at the bottom.