Semantic parsing using deep neural networks for predicting canonical forms

A method comprising:

• providing a neural network model which has been trained to predict a canonical form for an input text sequence, the neural network model comprising:
• an encoder which comprises a first multilayer perceptron which generates a first representation of the input text sequence based on a representation of n-grams in the text sequence, and
• a first recurrent neural network which reads the input text sequence and generates a second representation of the input text sequence , and
• a decoder which sequentially predicts a next term of the canonical form, based on the first and second representations and a predicted prefix of the canonical form;
• receiving an input text sequence;
• with a processor, predicting a canonical form for the input text sequence with the trained neural network model;
• and
• outputting information based on the predicted canonical form.

A system comprising:

• memory which stores a neural network model which has been trained to predict a canonical form for an input text sequence, the neural network model comprising:
• an encoder which generates a first representation of the input text sequence based on a representation of n-grams in the text sequence and a second representation of the input text sequence generated by a first neural network, and
• a decoder which sequentially predicts terms of the canonical form based on the first and second representations and a predicted prefix of the canonical form;
• a prediction component which predicts a canonical form for an input text sequence with the trained neural network model;
• a semantic parser which generates a logical form based on the predicted canonical form;
• an output component which outputs information based on the predicted canonical form;
• and
• a processor which implements the prediction component and the output component.

A method for predicting a canonical form comprising:

• providing training data, the training data comprising a collection of training pairs, each training pair in the collection including a canonical form, containing a sequence of words, and a corresponding text sequence, containing a sequence of words;
• with the training data, training a neural network model to predict a canonical form, containing a sequence of words, for an input text sequence, the neural network model comprising:
• an encoder which generates a first representation of the input text sequence based on a representation of n-grams in the text sequence and a second representation of the input text sequence generated by a first neural network, and
• a decoder which sequentially predicts terms of the canonical form based on the first and second representations and a predicted prefix of the canonical form, each of the terms of the canonical form including at least one word;
• receiving an input text sequence, containing a sequence of words;
• with a processor, predicting a canonical form, containing a sequence of words, for the input text sequence with the trained neural network model;
• and
• outputting information based on the predicted canonical form.