Method for generating cross-lingual textual semantic model, and electronic device

An electronic device, comprising:

• a processor;
• and
• a memory for storing instructions executable by the processor;
• wherein the processor is configured to:
• acquire a set of training data, wherein the set of training data comprises pieces of monolingual non-parallel text and pieces of bilingual parallel text;
• determine a semantic vector of each piece of text in the set of training data by inputting each piece of text in the set of training data into an initial textual semantic model;
• determine a distance between semantic vectors of each two pieces of text in the set of training data based on the semantic vector of each piece of text in the set of training data;
• determine a gradient modification based on a parallel relationship between each two pieces of text in the set of training data and the distance between the semantic vectors of each two pieces of text in the set of training data;
• and
• acquire a modified textual semantic model by modifying the initial textual semantic model based on the gradient modification.

A non-transitory computer-readable storage medium stored with computer instructions, wherein, the computer instructions are configured to cause a computer to perform a method for generating a cross-lingual textual semantic model, the method comprising:

• acquiring a set of training data, wherein the set of training data comprises pieces of monolingual non-parallel text and pieces of bilingual parallel text;
• determining a semantic vector of each piece of text in the set of training data by inputting each piece of text in the set of training data into an initial textual semantic model;
• determining a distance between semantic vectors of each two pieces of text in the set of training data based on the semantic vector of each piece of text in the set of training data;
• determining a gradient modification based on a parallel relationship between each two pieces of text in the set of training data and the distance between the semantic vectors of each two pieces of text in the set of training data;
• and
• acquiring a modified textual semantic model by modifying the initial textual semantic model based on the gradient modification.