Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study
Table 1: Chosen hyperparameters for our POS tagging and parsing models. both means the feature is common to both the POS tagger and parser.
['[BOLD] POS Tagger Architecture  [BOLD] Parameter', '[BOLD] POS Tagger Architecture  [BOLD] Value']
For POS tagging, we adopt a standard architecture with a word and character-level Bi-LSTM Plank et al. These representations are passed to a multilayer perceptron (MLP) classifier followed by a softmax function to choose a tag with the highest probability. For both the POS tagging and parsing models, we use a word embedding dimension of size 100 and a character embedding dimension of size 64. POS tag embeddings of dimension 50 are included in the parser. We train our Faroese models for fifty epochs. We do not split the synthetic Faroese treebanks into training/development portions though we suspect doing so will help the models to not overfit on the training data. For all experiments we report labelled attachment scores produced by the official CoNLL 2018 evaluation script.