Core Semantic First: A Top-down Approach for AMR Parsing The work described in this paper is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418). The first author is grateful for the discussions with Zhisong Zhang and Zhijiang Guo.
Table 3: Hyper-parameters settings.
['model component', 'hyper-parameter', 'value']
In all experiments, we use the same char-level CNN settings in the sentence encoder and the graph encoder. In addition, all Transformer Vaswani et al. For computation efficiency, we only allow each concept to attend to its previously generated concepts in the graph encoder. To mitigate overfitting, we also apply dropout Srivastava et al. We use a special UNK token to replace the input lemmas, POS tags, and NER tags with a rate of 0.33. Parameter optimization is performed with the Adam optimizer Kingma and Ba The same learning rate schedule of Vaswani et al. We use early stopping on the development set for choosing the best model.