Transfer Learning for Neural Semantic Parsing
Table 1: parameter size and training time comparision for independent and multi-task models
['[BOLD] Model architecture', '[BOLD] param. size', '[BOLD] step time']
As expected, we see comparable training time for one-to-many and one-to-shareMany, but 10% step time increase for one-to-one. We also see that one-to-one and one-to-shareMany have similar number of parameter, which is about 15% smaller than one-to-many due to the sharing of weights. The one-to-shareMany architecture is able to get the increased sharing while still maintaining reasonable training speed per step-size.