Method and apparatus for question answering based on table, and electronic device

A method for training a question answering model, comprising:

• constructing an initial question answering model, wherein, the question answering model is configured to perform a vector extraction on a question text and on an attribute name in an information table, to acquire a vector extraction module configured to extract a character vector sequence, a position vector sequence and a type vector sequence, a semantic extraction module configured to extract a semantic representation vector sequence, to splice the semantic representation vector of the question text character in the semantic representation vector sequence and the semantic representation vector of the attribute name, and to predict an attribute name segment and an attribute value segment in the question text;
• and
• acquiring training data, wherein, the training data comprises:
• at least one attribute name in the information table, a sample question text, and a sample attribute name segment and a sample attribute value segment corresponding to the sample question text;
• and
• training the question answering model by taking the sample question text and the at least one attribute name as inputs of the question answering model and the sample attribute name segment and the sample attribute value segment as outputs of the question answering model.

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected to the at least one processor;
• wherein,
• the memory is stored with instructions executable by the at least one processor, and when the instructions are performed by the at least one processor, the at least one processor is caused to:
• determine a question text to be processed and an information table for question answering, wherein, the information table comprises:
• at least one attribute name;
• determine a character vector sequence, a position vector sequence and a type vector sequence based on the question text and the at least one attribute name;
• determine an attribute name segment and an attribute value segment in the question text based on the character vector sequence, the position vector sequence and the type vector sequence;
• and
• determine an answer corresponding to the question text based on the attribute name segment, the attribute value segment and the information table.