Tencent Technology (shenzhen) Company Limited

Shuming Shi

Zhaopeng Tu

Tong Zhang

Yan Song

Yan Wang

Jing Tian Li

Hao Zhou

Xiaojiang Liu

Xiang Kong

Haisong Zhang

Lemao Liu

Xintong Li

Jialong Han

A translation method is provided, including: encoding to-be-processed text information to obtain a source vector representation sequence, the to-be-processed text information belonging to a first language; obtaining a source context vector corresponding to a first instance according to the source vector representation sequence, the source context vector indicating to-be-processed source content in the to-be-processed text information at the first instance; determining a translation vector according to the source vector representation sequence and the source context vector; and decoding the translation vector and the source context vector, to obtain target information of the first instance, the target information belonging to a second language.

Translation method, target information determining method, related apparatus, and storage medium

A machine translation method includes: receiving to-be-processed information expressed in a source language; encoding the to-be-processed information, and generating an expression vector sequence of the to-be-processed information; and predicting feature information of a target foresight word at a first moment by using a prediction model. The feature information includes at least one of a part of speech or a word category of the target foresight word. The method also includes: determining a context vector corresponding to the first moment in the expression vector sequence according to the feature information of the target foresight word; and decoding the context vector by using a decoder, to obtain target content that corresponds to the context vector and expressed in a target language.

Machine translation method, device, and computer-readable storage medium

Embodiments of this application provide a message digest generation method and apparatus, and a storage medium. The generation method is performed by an electronic device, and includes: obtaining a plurality of associated messages from a to-be-processed message set; generating a function label distribution model, a sentiment label distribution model, a word category label distribution model, and a word sentiment polarity label distribution model corresponding to each of the plurality of associated messages; determining, based on the function label distribution model, the sentiment label distribution model, the word category label distribution model, and the word sentiment polarity label distribution model, a distribution probability that a category of a word included in the plurality of associated messages is a subject content word; and generating a digest of the plurality of associated messages according to the distribution probability of the subject content word.

Method and apparatus for generating digest for message, and storage medium thereof

Embodiments of the present application disclose a mathematical processing method for a textual question, an apparatus, a computer device, and a computer storage medium. A numeral in a question text sequence is mapped to an expression word, a question text sequence including the expression word is encoded into a semantic vector, an expression is generated by using the semantic vector including question information, and the expression word in the expression is replaced with the mapped numeral, so that the obtained mathematical expression, compared with a preset fixed expression template, is more accurate, is more likely to meet question-answering requirements, and can improve intelligent question-answering accuracy.

Mathematical processing method, apparatus and device for text problem, and storage medium

Embodiments of this application disclose a hyperlink processing method and apparatus. The method includes: converting first context information of a first hyperlink in a first object into a first context vector; obtaining a first input vector of the first object, the first object including information on the first hyperlink pointing to a second object; obtaining a first average vector according to the first context vector and the first input vector; adjusting at least one of the first input vector, the first context vector, or a first output vector corresponding to the second object; and calculating a similarity between the first output vector and the first average vector according to an adjustment result, and outputting the first output vector as an output vector of the second object in response to the similarity being greater than or equal to a first target threshold.

Hyperlink processing method and apparatus

A machine translation method and an apparatus are provided. The method includes: obtaining, when translating a word fragment from a first language into a second language, a source representation vector of the word fragment. The source representation vector of the word fragment represents the word fragment in the first language. The method also includes obtaining a historical translation vector of the word fragment by querying historical translation information according to the source representation vector of the word fragment. The historical translation vector of the word fragment represents a historical translation situation corresponding to the word fragment. The method further includes translating the word fragment according to the historical translation vector of the word fragment.

Machine translation method, apparatus, computer device, and storage medium

A translation model based training method is provided for a computer device. The method includes inputting a source sentence to a translation model, to obtain a target sentence outputted by the translation model; determining a fidelity of the target sentence to the source sentence; using the target sentence and a reference sentence as input of a discriminator model, using the fidelity as output of the discriminator model, and training the discriminator model on a performance of calculating a similarity between the target sentence and the reference sentence; outputting the similarity by using the discriminator model; and using the source sentence as input of the translation model, using the target sentence as output of the translation model, and using the similarity as a weight coefficient, and training the translation model on a performance of outputting the corresponding target sentence according to the input source sentence.

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Shenzhen, China

Shuming Shi