The present disclosure is directed to embodiments directed to systems and methods for policy management. In some implementations, a master policy management system can create a policy template in which all policies of a user can be built, monitored, and enforced. The master policy management system can create a taxonomy for the policy template and receive access and control settings for the policy template from the user. A user can generate policies in the policy template and the master policy management system can review and certify the policies based the accuracy of the policies. Once a policy is built, the master policy management system can review and certify the policy, provide a quality score for the policy, perform lifecycle management, record the policy use, and report alerts regarding the policy.

The present disclosure is directed to embodiments directed to systems and methods for policy management. In some implementations, a master policy management system can create a policy template in which all policies of a user can be built, monitored, and enforced. The master policy management system can create a taxonomy for the policy template and receive access and control settings for the policy template from the user. A user can generate policies in the policy template and the master policy management system can review and certify the policies based the accuracy of the policies. Once a policy is built, the master policy management system can review and certify the policy, provide a quality score for the policy, perform lifecycle management, record the policy use, and report alerts regarding the policy.

Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.

Systems and methods are presented for the automatic placement of rules applied to topics in a logical hierarchy when conducting natural language processing. In some embodiments, a method includes: accessing, at a child node in a logical hierarchy, at least one rule associated with the child node; identifying a percolation criterion associated with a parent node to the child node, said percolation criterion indicating that the at least one rule associated with the child node is to be associated also with the parent node; associating the at least one rule with the parent node such that the at least one rule defines a second factor for determining whether the document is to also be classified into the parent node; accessing the document for natural language processing; and determining whether the document is to be classified into the parent node or the child node based on the at least one rule.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that translate text depicted in images from a source language into a target language. Methods can include obtaining a first image that depicts first text written in a source language. The first image is input into an image translation model, which includes a feature extractor and a decoder. The feature extractor accepts the first image as input and in response, generates a first set of image features that are a description of a portion of the first image in which the text is depicted is obtained. The first set of image features are input into a decoder. In response to the input first set of image features, the decoder outputs a second text that is a predicted translation of text in the source language that is represented by the first set of image features.

A customer is detected at a drive-thru and a natural language voice dialogue session is established with the customer. The customer provides voice inquiries and order details via speech during the session, the speech is translated to text sentences, and commands are issued to a transaction system through an Application Programming Interface (API) based on the text of the sentences. The transaction system updates a display associated with the drive-thru based on the commands processed for the session and places an order for the customer with a Point-Of-Sale (POS) terminal associated with the drive-thru based on the order details.

A customer is detected at a drive-thru and a natural language voice dialogue session is established with the customer. The customer provides voice inquiries and order details via speech during the session, the speech is translated to text sentences, and commands are issued to a transaction system through an Application Programming Interface (API) based on the text of the sentences. The transaction system updates a display associated with the drive-thru based on the commands processed for the session and places an order for the customer with a Point-Of-Sale (POS) terminal associated with the drive-thru based on the order details.

A method of generating an editable translated electronic document from an input image of an original document with a first layout includes: segmenting the input image to generate a first region including first untranslated text; extracting, from the first region, the first untranslated text and a first layout information; generating an editable output data including the first untranslated text and the first layout information; translating the first untranslated text into a translated text; editing the output data to include the translated text; and generating, using the first layout information, the translated electronic document including the translated text and a second layout that is identical to the first layout.

A method of generating an editable translated electronic document from an input image of an original document with a first layout includes: segmenting the input image to generate a first region including first untranslated text; extracting, from the first region, the first untranslated text and a first layout information; generating an editable output data including the first untranslated text and the first layout information; translating the first untranslated text into a translated text; editing the output data to include the translated text; and generating, using the first layout information, the translated electronic document including the translated text and a second layout that is identical to the first layout.

In methods for training a natural language generation (NLG) model using a processor a document-level machine translation (MT) model is provided by training an MT model to receive as input, token sequences in a first language, and to generate as output, token sequences in a second language. An augmented document-level MT model is provided by training the document-level MT model to receive as input, paired language-independent structured data and token sequences in the first language, and to generate as output, token sequences in the second language. The augmented document-level MT model is trained to receive as input, language-independent structured data, and to generate as output, token sequences in the second language.