AMR Parsing via Graph\leftrightarrowsSequence Iterative Inference The work described in this paper is substantially supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418) and the Direct Grant of the Faculty of Engineering, CUHK (Project Code: 4055093).
Table 3: Hyper-parameters settings.
['[BOLD] Embeddings lemma', '[BOLD] Embeddings 300']
Char-level CNNs and Transformer layers in the sentence encoder and the graph encoder share the same hyper-parameter settings. The BERT model Devlin et al. we used is the Huggingface’s implementation Wolf et al. (bert-base-cased). To mitigate overfitting, we apply dropout Srivastava et al. We randomly mask (replacing inputs with a special UNK token) 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 learning rate schedule is similar to that in \newcitevaswani2017attention, with warm-up steps being set to 2K. We use early stopping on the development set for choosing the best model.