Microsoft Technology Licensing, LLC

PalaniRaj Kaliyaperumal

Kuralmani Elango

Kunal Mukerjee

Jian-guang Lou

Weizhu Chen

Haiyuan Cao

Yu Hu

Zeqi Lin

Yi Liu

Lily Xinchen Liu

Examples described herein generate training data for machine learning (ML) for natural language (NL) processing (such as semantic parsing for translating NL). A formula tree is generated based on sampling both a formula grammar and NL templates. Using the formula tree, an ML training data instance pair is generated comprising a formula example and an NL example. A context example may also be used during instantiation of the formula tree. An ML model is trained with training data including the ML training data instance pair, and ML output is generated from NL input. The ML output includes, for example, a machine-interpretable formula, a database querying language command, or a general programming language instruction. Some examples support context-free grammar, probabilistic context-free grammar, and/or non-context-free production rules.

Generating model training data from a domain specification

The disclosure herein describes generating an application from input content. Input content of a content type is obtained, such as an image file, digital document file, or the like. A content data extractor is selected from a set of content data extractors based on the content type. A set of content entities is extracted from the obtained input content using the selected content data extractor, such as text labels, text boxes, buttons, or the like. The set of content entities are normalized according to a standard interface schema and an application template is generated using the normalized set of content entities, whereby an application can be developed using the generated application template. The disclosure enables application interfaces to be designed using a variety of methods and for those different types of designs to be efficiently converted to a functional application.

Creating applications and templates based on different types of input content

Methods, systems, and computer program products are provided for dynamic and adaptive user-interface component selection for rendering in distributed applications. An adaptive user-interface component selection system is enabled to select user-interface components for rendering and display on a client device running an instance of a client application. User-interface components are selected based on a set of applicable component rules. Applicable component rules are determined based on client feedback information that includes information regarding the client including: network bandwidth, geolocation, user role, user context and required application features. Selected user-interface components are transformed into renderable code either locally to the system or remotely at the client device, and rendered and displayed on the client device.

Adaptive user-interface assembling and rendering

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Redmond, WA, United States of America

PalaniRaj Kaliyaperumal