Beijing Baidu Netcom Science and Technology Co., Ltd.

International Business Machines Corporation

Baidu Online Network Technology (beijing) Co., Ltd.

Xiao Yin Zhou

Xin Peng Liu

Dianhai Yu

Shiqi Zhao

Zhenni Yan

Liwei Chen

Ya Ling Xu

Lei Han

Tian Wu

Qian Xu

Chao Feng

Huan Tang

Wensong He

Liangcheng Wu

Guangya Zhu

The present application disclosures a voice skill starting method, an apparatus, a device, and a storage medium, and relates to the field of artificial intelligence. An implementation scheme is that: the method is applied to an electronic device including at least one third-party voice skill and a built-in voice skill, and the electronic device is currently in the built-in voice skill. The method includes: receiving a current demand instruction of a user; judging whether the current demand instruction belongs to an entry demand instruction corresponding to the third-party voice skill according to a mapping relationship in response to the current demand instruction; and switching from the built-in voice skill to the third-party voice skill if it is determined that the current demand instruction belongs to the entry demand instruction corresponding to the third-party voice skill.

Voice skill starting method, apparatus, device and storage medium

The present disclosure proposes a method and an apparatus for recommending sample data. The method may include: inputting a plurality of pieces of sample data to be classified into at least one preset classification model, and acquiring a classifying probability of classifying each piece of sample data into each classification model; acquiring a first distance between each piece of sample data and a classifying boundary of each classification model according to the classifying probability of classifying the piece of sample data into the classification model, in which the classifying boundary of the classification model is configured to distinguish positive and negative sample data; computing a target distance for each piece of sample data according to the first distance between each piece of sample data and the classifying boundary of each classification model.

Method and apparatus for recommending sample data

A method for exiting a voice skill, an apparatus, a device, and a storage medium are provided by embodiments of the present disclosure, wherein a user voice instruction is received; a target exit intention corresponding to the user voice instruction is identified according to the user voice instruction and a grammar rule of a preset exit intention; and a corresponding operation is executed on a current voice skill of a device according to the target exit intention. The embodiments of the present disclosure refine and expand the user's exit intention. After the target exit intention to which the user voice instruction belongs is identified, the corresponding operation is executed according to the target exit intention so as to meet the users' different exit requirements for the voice skills, enhance the fluency and convenience of user interaction with the device and improve the user's exit experience when using the voice skills.

Method for exiting a voice skill, apparatus, device and storage medium

A method, computer system, and a computer program product may perform document reference and reference update. One or more processors may assign marker information for a reference of a reference source. The reference may reference a target portion of a target document. The one or more processors may determine identification information for the target portion. The determined identification information may be based on content in the target portion and context information for the target portion in the target document. The one or more processors may generate a mapping of at least the marker information, the identification information, and a relative location of the target portion within the target document for use in the referencing of the target portion by the reference source.

Document reference and reference update

A plurality of metadata corresponding to a plurality of code versions of an application stored in a version control system is generated. A determination is made of a set of changes between a first metadata of a first code version and a second metadata of a second code version. A classification is made of elements in the set of changes into a first category and a second category based on a set of predetermined rules, wherein the elements classified into the first category are better candidates to determine causes of defects in the application than the elements classified into the second category. The elements classified in the first category are used to determine a cause of a defect in the application.

Version control mechanisms augmented with semantic analysis for determining cause of software defects

A query method and apparatus. A specific implementation of the method comprises: acquiring a target historical query statement from a historical query statement collection; extending the target historical query statement based on a query log to generate an extended statement fragment associated with the target historical query statement; adding the extended statement fragment, as a statement fragment characteristic associated with the target historical query statement, into a statement fragment characteristic library of a preset model; and generating a query result corresponding to a query statement based on the statement fragment characteristic library of the preset model in response to receiving the query statement. According to an embodiment of the present disclosure, the extended statement fragment is added, as the statement fragment characteristic associated with the target historical query statement, into the statement fragment characteristic library of the preset model.

Query method and apparatus

The present disclosure discloses an interaction method and apparatus based on artificial intelligence. A specific embodiment of the method comprises: receiving a current interactive statement entered by a user through a terminal; extracting at least one type of characteristic based on the current interactive statement and a previous interactive statement entered by the user; processing the at least one type of characteristic using a pretrained neural network model to determine whether an intent maintaining relationship exists between the current interactive statement and the previous interactive statement; and if the intent maintaining relationship exists, updating a limitation condition for the previous interactive statement using the current interactive statement, performing information retrieval using the previous interactive statement with the updated limitation condition, and pushing a retrieved retrieval result to the terminal. This embodiment reduces time spent in user entry.

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Beijing, China

Xiao Yin Zhou