Bayerische Motoren Werke Aktiengesellschaft

Wangsu Hu

Jilei Tian

Embodiments relate to a method, a computer program and a system for optimizing execution of natural language to structured query language. The method comprises the steps of&#x2014;receiving a natural language text input and performing natural language processing on the natural language text input to generate a plurality of encoded question tokens. Next, the method comprises performing natural language processing on a plurality of table schema stored in a database to generate a plurality of encoded table schema tokens for each table schema. Further, the method comprises determining a similarity between the plurality of encoded question tokens and the plurality of encoded table schema tokens for at least two table schemas of the plurality of table schema. Furthermore, the method comprises determining an output table schema from the plurality of table schema based on the similarity and outputting a natural language string based on the output table schema.

Natural language processing of encoded question tokens and encoded table schema based on similarity

A method for providing responsive actions to user inputs in a multi-domain context includes receiving, by a speech-based user interface, a first speech input from a user and converting said first speech input into a text-based representation of the first speech input. A natural language processor processes the text-based representation to determine an intent, entity and internal state of the first speech input. The method further includes determining, by a model-based module based on the intent, entity and internal state, a first data processing policy to apply to the first speech input, wherein the first data processing policy is either a rules-based data processing policy applied by a rules-based module or a statistical model-based data processing policy applied by the model-based module. The first responsive action is generated by the determined first data processing module, and outputted via the speech-based user interface and/or a machine interface.

Hybrid policy dialogue manager for intelligent personal assistants

The present disclosure relates to a concept for machine-learning-based prediction of a destination for a user. Based on historic search data associated with the user, at least one candidate destination is determined based on the user and a given context. A plurality of embedding vectors are determined from an embedding matrix, wherein the embedding vectors are associated with the at least one candidate destination, the user, and the given context. The embedding matrix comprising embedding vectors for different components of the historic search data. The plurality of embedding vectors are fed into one or more first neural network layers to generate a semantic embedding for the candidate destination. The semantic embedding is into one or more second neural network layers to generate a probability score for the candidate destination.

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Chicago, IL, United States of America

Wangsu Hu