Beijing Baidu Netcom Science and Technology Co., Ltd.

Huawei Technologies Co., Limited

Beijing Bytedance Network Technology Co., Ltd.

Baidu USA LLC

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

Hao Xiong

Haifeng Wang

Zhongjun He

Hua Wu

Zhi Hong Li

Chuanqiang Zhang

Ruiqing Zhang

Zhou Xin

Kai Liu

Lin Ma

Lin Wang

Tian Wu

Junjie Li

Xiaoguang Hu

Xing Li

Bin Yang

Jingui Wang

Mengluo Feng

Hairong Liu

Dawei Chen

Shuai Zhao

Mingbo Ma

Liang Huang

Renjie Zheng

Kaibo Liu

Baigong Zheng

Tianchi Bi

Peiqi Wu

Electric bicycle frame

Electric bicycle

The present disclosure provides a method, apparatus, electronic device and readable storage medium for translation and relates to translation technologies. In the embodiments of the present disclosure, the at least one knowledge element is obtained according to associated information of content to be translated, and respective knowledge element in the at least one knowledge element comprise an element of the first language type and an element of the second language type so that the at least one knowledge element can be used to obtain a translation result of the content to be translated. Since the at least one knowledge element obtained in advance is taken as global information of the translation task of this time, it can be ensured that the translation result of the same content to be translated is consistent, thereby improving the quality of the translation result.

Method, apparatus, electronic device and readable storage medium for translation

Embodiments of the present disclosure relate to a method and apparatus for evaluating a translation quality. The method may include: acquiring a to-be-evaluated translation and a reference translation; inputting the to-be-evaluated translation and the reference translation into a pre-trained repetition coding model to obtain a semantic similarity between the to-be-evaluated translation and the reference translation, the repetition coding model being a neural network for calculating a probability of a pair of sentences being repetition sentences; analyzing the to-be-evaluated translation and the reference translation into two syntax trees respectively; calculating a similarity between the two syntax trees as a text similarity between the to-be-evaluated translation and the reference translation; and using a weighted sum of the semantic similarity and the text similarity as a translation quality score.

Method and apparatus for evaluating translation quality

Disclosed is a video playing processing method. The method includes: receiving prompt information for playing a second video associated with a first video sent by an application server, the first video is a video being currently played, and the first video and the second video include a same video content segment; and showing the prompt information for playing the second video associated with the first video. Disclosed are also a terminal device, a server, and a storage medium.

Video playback processing method, terminal device, server, and storage medium

Embodiments of the present disclosure provide a language conversion method and apparatus based on syntactic linearity and a non-transitory computer-readable storage medium. The method includes: encoding a source sentence to be converted by using a preset encoder to determine a first vector and a second vector corresponding to the source sentence; determining a current mask vector according to a preset rule, in which the mask vector is configured to modify vectors output by the preset encoder; determining a third vector according to target language characters corresponding to source characters located before a first source character; and decoding the first vector, the second vector, the mask vector, and the third vector by using a preset decoder to generate a target character corresponding to the first source character.

Language conversion method and apparatus based on syntactic linearity, and non-transitory computer-readable storage medium

The present disclosure provides a translation processing method, a translation processing device, and a device. The first speech signal of the first language is obtained, and the speech feature vector of the first speech signal is extracted based on the preset algorithm. Further, the speech feature vector is input into the pre-trained end-to-end translation model for conversion from the first language speech to the second language text for processing, and the text information of the second language corresponding to the first speech signal is obtained. Moreover, speech synthesis is performed on the text information of the second language, and the corresponding second speech signal is obtained and played.

Translation processing method, translation processing device, and device

Embodiments of the present disclosure disclose a text translation method, a text translation apparatus, a device and a storage medium. The method includes: obtaining a source language text; and translating the source language text with a modified translation model to obtain a target language text corresponding to the source language text, the modified translation model being obtained by modifying an original translation model based on a text evaluation result of one or more translated texts for training, the translated text for training being an output result after translating through the original translation model, and the text evaluation result for evaluating a contextual semantic relation in the translated text for training.

Text translation method, device, and storage medium

Embodiments of the present disclosure provide a method and an apparatus for translating a polysemy, and a medium. The method includes: obtaining a source language text; identifying and obtaining the polysemy from the source language text; inquiring related words corresponding to each interpretation of the polysemy; determining a target interpretation corresponding to the related words contained in the source language text; and translating the polysemy into the target interpretation.

Method and apparatus for translating polysemy, and medium

The present disclosure provides an end-to-end model training method and apparatus, which relates to a field of artificial intelligence technologies. The method includes: obtaining training data containing a plurality of training samples, in which the plurality of training samples include an original sequence, a target sequence and a corresponding tag list, the tag list includes importance tags in the target sequence and avoidance tags corresponding to the importance tags, and the avoidance tags are irrelevant tags corresponding to the importance tags; and adopting the training data to train a preset end-to-end model until a value of a preset optimization target function is smaller than a preset threshold.

End-to-end model training method and apparatus, and non-transitory computer-readable medium

A method and apparatus for translating speech are provided. The method may include: recognizing received to-be-recognized speech of a source language to obtain a recognized text; concatenating the obtained recognized text after a to-be-translated text, to form a concatenated to-be-translated text; inputting the concatenated to-be-translated text into a pre-trained discriminant model to obtain a discrimination result for characterizing whether the concatenated to-be-translated text is to be translated, where the discriminant model is used to characterize a corresponding relationship between a text and a discrimination result corresponding to the text; in response to the positive discrimination result being obtained, translating the concatenated to-be-translated text to obtain a translation result of a target language, and outputting the translation result.

Method and apparatus for translating speech

Presented herein are embodiments of a prefix-to-prefix framework for simultaneous translation that implicitly learns to anticipates in a single translation. Within these frameworks are effective 'wait-k' policy model embodiments that may be trained to generate a target sentence concurrently with a source sentence but lag behind by a predefined number of words. Embodiments of the prefix-to-prefix framework achieve low latency and better quality when compared to full-sentence translation in four directions: Chinese&#x2194;English and German&#x2194;English. Also presented herein is a novel latency metric that addresses deficiencies of previous latency metrics.

Systems and methods for simultaneous translation with integrated anticipation and controllable latency (STACL)

A semantic similarity evaluation method includes performing word vectorization processing separately on words in a first sentence and a word in a second sentence to obtain a first word vector and a second word vector; performing, in a preset word vector compression order, compression coding processing on the first word vector according to a first compression coding parameter to obtain a first statement vector; performing, in the preset word vector compression order, compression coding processing on the second word vector according to a second compression coding parameter to obtain a second statement vector; and determining a vector distance between the first statement vector and the second statement vector, and evaluating a semantic similarity between the first sentence and the second sentence according to the vector distance. The method is used to evaluate a semantic similarity.

Semantic similarity evaluation method, apparatus, and system

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Beijing, China

Hao Xiong