Huawei Technologies Co., Limited

Pascal Poupart

Zhitang Chen

Yanhui Geng

Mehdi Rezagholizadeh

Md Akmal Haidar

Vahid Partovi Nia

A method and machine translation system for bi-directional translation of textual sequences between a first language and a second language are described. The machine translation system includes a first autoencoder configured to receive a vector representation of a first textual sequence in the first language and encode the vector representation of the first textual sequence into a first sentence embedding. The machine translation system also includes a sum-product network (SPN) configured to receive the first sentence embedding and generate a second sentence embedding by maximizing a first conditional probability of the second sentence embedding given the first sentence embedding and a second autoencoder receiving the second sentence embedding, the second autoencoder being trained to decode the second sentence embedding into a vector representation of a second textual sequence in the second language.

System and method for bi-directional translation using sum-product networks

A method for detecting a data flow type includes obtaining a header of a first data packet of a current data flow and a pattern vector of the current data flow from the header; comparing the at least one feature dimension in the pattern vector of the current data flow with a corresponding feature dimension in a pattern vector of at least one historical data flow, so as to obtain at least one pattern similarity of the current data flow; predicting a length of the current data flow according to the at least one pattern similarity of the current data flow and a length of the corresponding at least one historical data flow; and comparing the predicted length of the current data flow with a preset threshold, and determining whether the current data flow is a large data flow or a small data flow according to a comparison result.

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Shenzhen, China

Pascal Poupart