Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
Table 4: Baseline Results Semantic Parsing on LDC2016E25.
['[BOLD] Baseline', '[BOLD] Type seq2seq', '[BOLD] Dev 54.8', '[BOLD] Diff', '[BOLD] Test 53.1', '[BOLD] Diff']
Compared to the F-score of 43.0 by Barzdins and Gosko This is probably due to the higher amount of training data and the fact that they used Tensorflow instead of OpenNMT. We also reproduced their results by using the exact same data, software and parameter settings as they did, obtaining an F1-score of 42.3. all increase the F-score by about a percentage point each. This small gain of performance is what one could expect as each single operation has only a small impact on the overall contents of an AMR.