International Business Machines Corporation

Maxwell Crouse

Achille Belly Fokoue-Nkoutche

Kavitha Srinivas

Lingfei Wu

Tamir Klinger

Maria Chang

Ibrahim Abdelaziz

Michael John Witbrock

Ryan A Musa

Cristina Cornelio

Bassem Makni

Veronika Thost

Ramon Fernandez Astudillo

Subhajit Chaudhury

Tahira Naseem

A decoder of a neural semantic parser receives input data associated with a natural language expression. An action is selected from a queue of actions, the queue of actions storing at least one action, the action being associated with an element from vocabulary of the natural language expression. The selected action is processed to build a tree structure where the processing of the selected action expands the tree structure with a node representing the element, where the tree structure is expanded bottom-up. A set of new actions is generated based on the node associated with the selected action and the vocabulary. The set of new actions is added to the queue of actions. The decoder repeats selecting, processing, generating and adding until a criterion is met. A logical form of the natural language expression is output based on the tree structure.

Bottom-up neural semantic parser

Generate, from a logical formula, a directed acyclic graph having a plurality of nodes and a plurality of edges. Assign an initial embedding to each mode and edge, to one of a plurality of layers. Compute a plurality of initial node states by using feed-forward networks, and construct cross-dependent embeddings between conjecture node embeddings and premise node embeddings. Topologically sort the DAG with the initial embeddings and node states. Beginning from a lowest rank, compute layer-by-layer embedding updates for each of the plurality of layers until a root is reached. Assign the embedding update for the root node as a final embedding for the DAG. Provide the final embedding for the DAG as input to a machine learning system, and carry out the automatic theorem proving with same.

Capturing the global structure of logical formulae with graph long short-term memory

Systems, computer-implemented methods, and computer program products that can facilitate encoding a tree data structure into a vector based on a set of constraints are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a constraint former that can form a set of constraints based on a first tree data structure and a vector encoder that can encode the first tree data structure into a vector based on the set of constraints.

Encoding and decoding tree data structures as vector data structures

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Maxwell Crouse