Xerox Corporation

Centre National De La Recherche Scientifique

Conduent Business Services, LLC

Chunyang Xiao

Marc Dymetman

Claire Gardent

Methods, systems, and devices for semantic parsing. In an example embodiment, a method for semantic parsing can include steps, operations, or instructions such as obtaining a data pair for learning, the data pair comprising logical form data and natural utterance data; acquiring grammar for targeted logical forms among the logical form data of the data pair; modeling data comprising other available prior knowledge utilizing WFSA (Weighted Finite State Automata); combining with the targeted logical forms with the data modeled comprising the other available prior knowledge to form a background; and exploiting the background on the data pair. Note that we do not 'learn' the background, but 'learn' the background-RNN (Recurrent Neural Network).

Symbolic priors for recurrent neural network based semantic parsing

A method for predicting a canonical form for an input text sequence includes predicting the canonical form with a neural network model. The model includes 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. The model also includes 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. The canonical form can be used, for example, to query a knowledge base or to generate a next utterance in a discourse.

Semantic parsing using deep neural networks for predicting canonical forms

A system and method are provided which employ a neural network model which has been trained to predict a sequentialized form for an input text sequence. The sequentialized form includes a sequence of symbols. The neural network model includes an encoder which generates a representation of the input text sequence based on a representation of n-grams in the text sequence and a decoder which sequentially predicts a next symbol of the sequentialized form based on the representation and a predicted prefix of the sequentialized form. Given an input text sequence, a sequentialized form is predicted with the trained neural network model. The sequentialized form is converted to a structured form and information based on the structured form is output.

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Chunyang Xiao