Neural Semantic Parsing over Multiple Knowledge-bases
Table 1: Test accuracy for all models on all domains, along with the number of parameters for each model.
['Model', 'Basketball', 'Blocks', 'Calendar', 'Housing', 'Publications', 'Recipes', 'Restaurants', 'Social', 'Avg.', '# Model Params']
Our strongest results come when parameter sharing is maximal (i.e., single encoder and single decoder), coupled with a one-hot domain representation at decoding time (DomainEncoding). In this case accuracy improves not only on average, but also for each domain separately. Moreover, the number of model parameters necessary for training the model is reduced by a factor of 7.