Ubtech Robotics Corp Ltd

Siemens Aktiengesellschaft

Byd Company Limited

Yulong Computer Telecommunication Scientific (shenzhen) Co., Ltd.

Shenzhen Byd Auto R&d Company Limited

Li Ma

Youjun Xiong

Stephan Biber

Qiang Wang

Yunfei Zhang

Yan Zhu

Lu Zhang

Mingju Li

Yajun Zhu

Weixing Xiong

Hai Jun Wang

Yun-Fei Zhang

Ya-Jun Zhu

Ming-Ju Li

Zeng He He

Jian Min Wang

Zhenyue Wu

A method includes: acquiring an input sentence in a first language in a current round of conversation; translating the input sentence in the first language to obtain an input sentence in a second language, according to dialogue contents in the first language and dialogue contents in the second language that have a mutual translation relationship with the dialogue contents in the first language in historical rounds of conversation; invoking a multi-round conversation generation model to parse the input sentence in the second language in the current round of conversation to generate an output sentence in the second language in the current round of conversation; translating the output sentence in the second language in the current round of conversation to obtain at least one candidate result in the first language; and determining an output sentence in the first language from the at least one candidate result in the first language.

Method for human-machine dialogue, computing device and computer-readable storage medium

The present disclosure discloses an entity recognition model training method and an entity recognition method as well as an apparatus using them. The entity recognition model training method includes: obtaining a training text and matching the training text with a database to obtain a plurality of matching results; processing the matching results to obtain a plurality of feature vectors corresponding to the matching results; obtaining a word vector of each word in the training text by processing the training text; and training an initial entity recognition model based on the feature vector and the word vector to obtain an entity recognition model. By using this training manner, the entity recognition model obtained can have an improved accuracy of entity recognition.

Entity recognition method and apparatus

The present disclosure provides a corpus cleaning method and a corpus entry system. The method includes: obtaining an input utterance; generating a predicted value of an information amount of each word in the input utterance according to the context of the input utterance using a pre-trained general model; and determining redundant words according to the predicted value of the information amount of each word, and determining whether to remove the redundant words from the input utterance. In such a manner, the objectivity and accuracy of corpus cleaning can be improved.

Corpus cleaning method and corpus entry system

The present disclosure provides a sentence generation method as well as a sentence generation apparatus and a smart device. The method includes: obtaining an input sentence; searching for structurally similar sentence(s) of each input sentence, where the structurally similar sentence(s) are structurally similar to the input sentence; finding semantically similar sentence(s) of the structurally similar sentence(s); parsing the input sentence and the structurally similar sentence(s) to obtain a subject block, a predicate block, and an object block to rewrite the semantically similar sentences to generate a new sentence; filtering the new sentence based on a preset filtering condition; and labeling the filtered new sentence as a semantically similar sentence of the input sentence. In this manner, a plurality of new sentences with different sentence patterns can be generated based on the same input sentence, which improves the controllability in generating the sentences and saves the labor cost therein.

Sentence generation method, sentence generation apparatus, and smart device

The present disclosure provides a preamble configuration method on an unlicensed carrier, which includes: PRACH configuration information. The PRACH configuration information includes sub-frame location information, CCA interval location information for a channel idle estimation, a channel detection mechanism, preamble format information and preamble sending location information; the sub-frame location information representing a sub-frame number of a sub-frame in which a preamble to be sent is located, the CCA interval location information representing a location on the sub-frame or on a previous sub-frame of the sub-frame, the CCA interval occupying at most N symbols at a head or at an end of the sub-frame, the preamble format information representing that the preamble is a format 0 preamble, the preamble sending location information representing a sending location of the format 0 preamble to be sent on the sub-frame. Control signaling carrying the PRACH configuration information to user equipment is sent.

Preamble configuration method, sending method and related devices on an unlicensed carrier

Methods of preparing negative active materials and negative active materials are provided herein. The preparation methods include: A) mixing a carbon material, an organic polymer, a Sn-containing compound&#x2014;optionally with water&#x2014;to obtain a mixed solution system; B) adding a complexing agent into the mixed solution system obtained in step A optionally while stirring to form an intermediate solution; C) adding a reducing agent into the intermediate solution obtained in step B to a reaction product; D) optionally filtering, washing and then drying the reaction product to obtain the negative active material.

Negative active materials, lithium ion batteries, and methods thereof

In a method for acquiring magnetic resonance signals from a first shoulder or an opposite second shoulder of a patient, a shoulder coil anterior part and a shoulder coil posterior part are provided. A base plate is engageable with a moveable support element connecting the shoulder coil posterior part to the base plate to permit lateral movement and 90 degree rotation of the shoulder coil posterior part. With the base plate beneath the patient, the shoulder coil posterior part is placed below the first shoulder and the shoulder coil anterior part is placed above the first shoulder to permit acquisition of magnetic resonance measurements. To acquire signals from the opposite second shoulder, the shoulder coil posterior part is moved across the base plate to be positioned beneath the second shoulder and is rotated by 90 degrees. The shoulder coil anterior part is placed above the rotated shoulder coil posterior part.

Method for use of a common shoulder coil part for both left and right shoulder

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Shenzhen, China

Li Ma