Microsoft Technology Licensing, LLC

Linjun Shou

Daxin Jiang

Ming Zhou

Shujie Liu

Jun Pan

Yumao Lu

Nan Duan

Duyu Tang

Yongjian Deng

Xuguang Wang

Ming Gong

Xuanyu Bai

Wenbin Cai

Jie Zhou

Wendi Wang

Baoquan Fan

The present disclosure proposes a method and apparatus for multi-model joint denoising training. Multiple models may be obtained. A set of training samples may be denoised through the multiple models. The multiple models may be trained with the set of denoised training samples.

Multi-model joint denoising training

A method and apparatus for providing recommended contents. The method comprises: A search query being received; an event being selected from a predetermined event set based on the search query; the recommended contents being provided in a structured template including one or more elements; each element is associated with a feature of the event; each feature of the event corresponds to a search intent. Each element includes one or more sub-contents, and each sub-content is correlated to corresponding recommended contents.

Providing recommended contents

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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Beijing, China

Linjun Shou