An abductive inference approach to cross-focus loss based on pre-trained models

Abstract

The present invention provides an abductive reasoning method based on the cross-focus loss of the pre-training model, including: the observation pair O 1 and O 2 Combining with all assumptions, the input sequence is obtained; input the single input variable in the input sequence into the pre-training model to obtain the feature matrix corresponding to the sentence level, and then sum the word dimensions of the feature matrix to obtain the feature vector; traverse all the input sequences in the input sequence A single input variable to obtain the feature vector sequence; input the feature vector sequence into the bidirectional long-term short-term memory network to obtain the distributed feature representation, and then use the fully connected layer to map and sum to obtain the score of each input; the N labels in the input sequence Form N groups with true values ​​and all false values, and perform softmax within the group to obtain cross-prediction values; introduce clustering factors and weight factors, improve FocalLoss, and obtain training loss functions; optimize the training loss function , to get the optimal abductive reasoning model.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, especially intelligent classification, for automatic classification and source tracing of language and events, and in particular to an abductive reasoning method based on cross-focus loss of pre-trained models. Background technique [0002] Human beings can understand natural language texts about everyday situations through past experience or known common sense. Given two observations O 1 and O 2 and two hypotheses H 1 and H 2 . Observing O 1 Under the condition, according to O 2 This result to guess leads to O 1 change to O 2 What are the causes of , and then from the candidate hypothesis H 1 , H 2 choose a more reasonable hypothesis. aNLI can also be said to look for the most plausible explanation. [0003] The purpose of abductive reasoning is mainly to help people understand texts and to capture whether there is a causal relationship between texts. The existing tech...

AI Technical Summary

Problems solved by technology

2) L2R 2 The method sorts these hypotheses according to the number of times they appear in the data set, but it is actually difficult to assign and sort the probability of occurrence. For these three hypotheses, we have no way to directly assign their probability of occurrence, and we There is also no way to judge which of the three hypotheses has the highest probability of occurrence and which has the lowest probability of occurrence

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