We present methods for multi-task learning that take advantage of natural groupings of related tasks. Task groups may be defined along known properties of the tasks, such as task domain or language. Such task groups represent supervised information …
The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient …
We present a zero-shot learning approach for text classification, predicting which natural language understanding domain can handle a given utterance. Our approach can predict domains at runtime that did not exist at training time. We achieve this …
We present a new model called LatticeRnn, which generalizes recurrent neural networks (RNNs) to process weighted lattices as input, instead of sequences. A LatticeRnn can encode the complete structure of a lattice into a dense representation, which …