Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
Table 1: Data splits of AMR 1.0, used in this work. Tokens are English, after tokenization.
['Corpus', 'Lines', 'Tokens']
We use English–AMR data from the AMR 1.0 corpus, LDC Catalog number 2014T12. The training set is used for rule extraction, language modeling, and statistical rule feature calculation. The development set is used both for parameter optimization and qualitatively for hill-climbing. The test set is held out blind for evaluation. We preprocess the English with a simple rule-based tokenizer and, except where noted, lowercase all data. We obtain English–AMR alignments by using the unsupervised alignment approach of \newcitepourdamghani-EtAl:2014:EMNLP2014. We compare our results to those of \newciteflanigan-etal: ACL2014 on the AMR 1.0 data splits; we run that work’s JAMR software according to the provided instructions.