Transfer Learning for Neural Semantic Parsing
Table 2: Accuracy on ATIS
['System', 'Test accuracy']
We use a single GRU layer of 128 hidden states to train the independent model. During transfer learning, we increase the model size to two hidden layers each with 512 hidden states. We adjust the minibatch size to 20 and dropout rate to 0.2 for independent model and 0.7 for multi-task model. We post-process the model output, balancing the braces and removing duplicates in the output. The initial learning rate has been adjusted to 0.8 using the dev set. Here, we only report accuracy numbers for the independent and one-to-shareMany frameworks. Correctness is based on denotation match at utterance level.