Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
Table 9: Comparison with previous parsers using the evaluation script of Damonte et al. (2017). We also included precision and recall scores for our system.
['[BOLD] Metric', '[BOLD] CAMR  [BOLD] F', '[BOLD] JAMR-16  [BOLD] F', '[BOLD] AMR-eager  [BOLD] F', '[BOLD] Our system  [BOLD] Pr.', '[BOLD] Our system  [BOLD] Rec.', '[BOLD] Our system  [BOLD] F']
Damonte et al. As the table shows, our system scores higher than the other parsers on five of the eight metrics other than Smatch. In general, our system is quite conservative, obtaining a higher precision than recall for each metric. Note that the other parsers score also relatively bad at these metrics. Compared to the other systems, our system scores worse on concepts, named entities, and wikification. A possible method to increase performance in the first two of those metrics is to adopt an anonymization or generalization approach for named entities and concepts, similar to Peng et al.