公开(公告)号：US11604774B2

公开(公告)日：2023-03-14

申请号：US17480294

申请日：2021-09-21

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Liujie Zhang ,  Yamei Li ,  Huihuang Zheng ,  Hongyu Liu ,  Xiang Lan ,  Dianhai Yu ,  Yanjun Ma ,  Tian Wu ,  Haifeng Wang

IPC: G06F16/00 ,  G06F16/21 ,  G06F16/22

Abstract: A method and apparatus of converting a schema in a deep learning framework, an electronic device, and a computer storage medium are provided. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema.

公开(公告)号：US20230058949A1

公开(公告)日：2023-02-23

申请号：US17657114

申请日：2022-03-29

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jizhou Huang ,  Shaolei Wang ,  Haifeng Wang

IPC: G10L13/047 ,  G10L15/22 ,  G10L15/06

Abstract: The present disclosure discloses an intelligent voice interaction method and apparatus, a device and a computer storage medium, and relates to voice, big data and deep learning technologies in the field of artificial intelligence technologies. A specific implementation solution involves: acquiring first conversational voice entered by a user; and inputting the first conversational voice into a voice interaction model, to acquire second conversational voice generated by the voice interaction model for the first conversational voice for return to the user; wherein the voice interaction model includes: a voice encoding submodel configured to encode the first conversational voice and historical conversational voice of a current session, to obtain voice state Embedding; a state memory network configured to obtain Embedding of at least one preset attribute by using the voice state Embedding; and a voice generation submodel configured to generate the second conversational voice by using the voice state Embedding and the Embedding of the at least one preset attribute. The at least one preset attribute is preset according to information of a verified object. Intelligent data verification is realized according to the present disclosure.

公开(公告)号：US20230004819A1

公开(公告)日：2023-01-05

申请号：US17930221

申请日：2022-09-07

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Yingqi Qu ,  Yuchen Ding ,  Jing Liu ,  Hua Wu ,  Haifeng Wang

IPC: G06N5/00 ,  G06F40/30 ,  G06F16/2457

Abstract: The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.

公开(公告)号：US20220391594A1

公开(公告)日：2022-12-08

申请号：US17820768

申请日：2022-08-18

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Haifeng Wang ,  Zhongjun He ,  Hua Wu ,  Zhanyi Liu ,  Zhi Li ,  Xing Wan ,  Jingxuan Zhao ,  Ruiqing Zhang ,  Chuanqiang Zhang ,  Fengtao Huang ,  Shuangshuang Cui ,  Yongzheng Xin

IPC: G06F40/30 ,  G06F40/58 ,  H04N5/278 ,  G06F40/166 ,  G06F40/279 ,  G06N5/02

Abstract: A display method, a method of training a semantic unit detection model, an electronic device, and a storage medium, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing and machine translation technologies. The display method includes: acquiring a language sequence to be displayed; dividing the language sequence to be displayed into a plurality of semantic units with semantics; and converting the plurality of semantic units into subtitles for display one by one.