Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Baidu USA LLC

Chuanqiang Zhang

Zhongjun He

Hua Wu

Haifeng Wang

Ruiqing Zhang

Hao Xiong

Zhi Hong Li

Zhi Li

Zhanyi Liu

Shuangshuang Cui

Yongzheng Xin

Xing Wan

Jingxuan Zhao

Fengtao Huang

Xing Li

Hairong Liu

Mingbo Ma

Liang Huang

Renjie Zheng

Tianchi Bi

Hanbing Song

Wei Di

Kaibo Liu

Baigong Zheng

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.

Display method, method of training semantic unit detection model, electronic device, and storage medium

A display method, an electronic device, and a storage medium, which relate to a field of natural language processing and a field of display. The display method includes: acquiring a content to be displayed; extracting a target term from the content using a term extraction rule; acquiring an annotation information for at least one target term, responsive to an extraction of the at least one target term; and displaying the annotation information for the at least one target term and the content.

Display method, electronic device, and storage medium

A method and apparatus for training models in machine translation, an electronic device and a storage medium are disclosed, which relates to the field of natural language processing technologies and the field of deep learning technologies. An implementation includes mining similar target sentences of a group of samples based on a parallel corpus using a machine translation model and a semantic similarity model, and creating a first training sample set; training the machine translation model with the first training sample set; mining a negative sample of each sample in the group of samples based on the parallel corpus using the machine translation model and the semantic similarity model, and creating a second training sample set; and training the semantic similarity model with the second training sample set.

Method and apparatus for training models in machine translation, electronic device and storage medium

A method for text error correction includes: obtaining a text to be corrected; obtaining a pinyin sequence of the text to be corrected; and inputting the text to be corrected and the pinyin sequence to a text error correction model, to obtain a corrected text.

Method and apparatus for text error correction, electronic device 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

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

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)

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Beijing, China

Chuanqiang Zhang