Generation of irrelevancy scores for input text

A method comprising steps to:

• receive input text;
• generate token sequences based on the input text;
• generate an encoder output by inputting the token sequences into a multi-layer bidirectional transformer to transform the token sequences into the encoder output, the multi-layer bidirectional transformer being trained using binary cross entropy loss on a set of ground truth token sequences which are labeled irrelevant for irrelevant text data which belongs in an irrelevant classification group, and labeled relevant for relevant text data which belongs in a relevant classification group;
• input the encoder output into a relevant linear function and an irrelevant linear function to linearly transform the encoder output, and output a transformed relevant output and a transformed irrelevant output, respectively;
• compute a relevance probability and a first irrelevance probability, respectively, by inputting the transformed relevant output and the transformed irrelevant output into a sigmoid function, the relevance probability being a probability that the token sequence belongs in the relevant classification group, the first irrelevance probability being a probability that the token sequence belongs in the irrelevant classification group;
• compute a second irrelevance probability by inputting the relevance probability and the first irrelevance probability into a tensor product formula;
• and
• generate and output an irrelevancy score for the input text based on the second irrelevance probability.

A computing system for classifying tax law articles, the computing system comprising:

• a processor and memory of a computing device, the processor being configured to execute a program using portions of memory to:
• receive input text from the tax law articles;
• generate token sequences based on the input text;
• generate an encoder output by inputting the token sequences into an encoder to transform the token sequences into the encoder output, the encoder being trained using binary cross entropy loss on a set of token sequences which are labeled with ground truth labels as first irrelevant for irrelevant text data which belongs in a first irrelevant classification group for irrelevant tax law articles which contain only income tax information or property tax information and no other tax information, labeled with ground truth labels as second irrelevant for irrelevant text data which belongs in a second irrelevant classification group for irrelevant tax law articles which contain only administrative tax information and no other tax information, and labeled with ground truth labels as relevant for relevant text data which belongs in a relevant classification group for relevant tax law articles which contain tax information other than administrative tax information, income tax information, or property tax information;
• input the encoder output into a relevant linear function, a first irrelevant linear function, and a second irrelevant linear function to linearly transform the encoder output, and output a transformed relevant output, a transformed first irrelevant output, and a transformed second irrelevant output, respectively;
• compute a relevance probability, a first irrelevance probability, and a third irrelevance probability, respectively, the relevance probability being a probability that the token sequence belongs in the relevant classification group, the first irrelevance probability being a probability that the token sequence belongs in the first irrelevant classification group, and the third irrelevance probability being a probability that the token sequence belongs in the second irrelevant classification group;
• compute a second irrelevance probability by inputting the relevance probability and the first irrelevance probability into a tensor product formula;
• compute a fourth irrelevance probability by inputting the relevance probability, the first irrelevance probability, and the third irrelevance probability into a product formula;
• and
• generate and output an irrelevancy score for the input text based on the fourth irrelevance probability.