Transition-based Semantic Dependency Parsing with Pointer Networks
Table 2: Model hyper-parameters.
['[BOLD] Architecture hyper-parameters', '[EMPTY]']
We do not specifically perform hyper-parameter selection for SDP and just adopt those proposed by Ma et al. For initializing word and lemma vectors, we use the pre-trained structured-skipgram embeddings developed by Ling et al. POS tag and character embeddings are randomly initialized and all embeddings (except the deep contextualized ones) are fine-tuned during training. Due to random initializations, we report average accuracy over 5 repetitions for each experiment. In addition, during a 500-epoch training, the model with the highest labelled F-score on the development set is chosen. Finally, while further beam-size exploration might improve accuracy, we use beam-search decoding with beam size 5 in all experiments.