Microsoft Technology Licensing, LLC

Duyu Tang

Ming Zhou

Neelakantan Sundaresan

Alexey Svyatkovskiy

Colin Bruce Clement

Shuai Lu

Nan Duan

Shujie Liu

Daxin Jiang

Linjun Shou

Ming Gong

Wendi Wang

Yuanhua Lv

A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Multi-lingual code generation with zero-shot inference

Implementations of the subject matter described herein provide a solution for generating a text from the structured data. In this solution, the structured data is converted into its representation, where the structured data comprises a plurality of cells, and the representation of the structured data comprises plurality of representations of the plurality of cells. A natural language sentence associated with the structured data may be determined based on the representation of the structured data, thereby implementing the function of converting the structured data into a text.

Generation of text from structured data

In embodiments of the present disclosure, there is provided an assertion-based question answering manner. After a question and the related passage are obtained, an assertion answer to the question is determined based on content of the passage, and the assertion answer has a predetermined structure and represents a complete semantic meaning. Then, the assertion answer to the question may be outputted to the user. In the embodiments of the present disclosure, the question and the relevant passage are used as input, and a semi-structured assertion answer is output. The assertion answer according to embodiments of the present disclosure can provide richer semantic content than the traditional short answer, and provide a more concise expression than the traditional long answer, thereby ensuring accuracy of the answer while improving the user experience.

Assertion-based question answering

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Duyu Tang