Abstract: An approach to determine the quality of encodings assigned to a word by a word embedding model. The approach may include determining the asymmetry of two embeddings associated with two words from a word embedding model. The asymmetry of the two words from a preexisting evocation dataset may be determined. The asymmetry of the two embeddings may be compared to the asymmetry from the evocation dataset to generate an encoding quality score.

Abstract: Systems and techniques that facilitate semantics-guided domain-specific data augmentation for text-to-graph parsing are provided. In various embodiments, a system can access an annotated training dataset, wherein the annotated training dataset can comprise a set of abstract meaning representation graphs respectively corresponding to a set of natural language sentences. In various aspects, the system can generate an augmented version of the annotated training dataset, based on applying semantics-guided composition operations or semantics-guided substitution operations to the set of abstract meaning representation graphs. In various instances, a lexicon legend can comprise domain-specific graphs respectively representing discrete tokens unique to a domain of the annotated training dataset.

Abstract: An approach to determine the quality of encodings assigned to a word by a word embedding model. The approach may include determining the asymmetry of two embeddings associated with two words from a word embedding model. The asymmetry of the two words from a preexisting evocation dataset may be determined. The asymmetry of the two embeddings may be compared to the asymmetry from the evocation dataset to generate an encoding quality score.

Abstract: An artificial intelligence (AI) platform to support random action replay for natural language (NL) learning. A NL conversation is explored to train a neural network. One or more tuples are leverage for the training, with each tuple representing an input action, a vector, an output action, and a reward value. An action is sampled from the vector, with the sampling including assessment of a corresponding first gradient. The first gradient is applied to selectively adjust the neural network. As NL input is received and applied to the selectively adjusted neural network, an output corresponding to the NL input is identified and a corresponding action is executed.