Pre-training model-based cause-tracing reasoning method for cross-focus loss

A reasoning method and pre-training technology, applied in the field of artificial intelligence, can solve problems such as low probability of occurrence, no way to judge, and assign values

Active Publication Date: 2021-08-20
INST OF AUTOMATION CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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  • Pre-training model-based cause-tracing reasoning method for cross-focus loss
  • Pre-training model-based cause-tracing reasoning method for cross-focus loss
  • Pre-training model-based cause-tracing reasoning method for cross-focus loss

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Embodiment 1

[0054] Such as figure 1 Shown, O 1 and O 2 For any observation pair, there are four corresponding hypotheses in this observation pair, where H 1 and H 2 For the correct assumption, H 3 and H 4 wrong assumption. The L2R2 ranking algorithm will rank these four probabilities and select the part with a higher probability as the correct answer. But there are still some flaws in it. First, for some hypotheses, we have no way to judge which one is more likely to be the answer, for example figure 1 H in 1 with H 2 , the meanings of these two sentences are not much different in essence, so it is difficult to compare their probabilities as answers. And for two unrelated answers, it is even more difficult to compare their probabilities as answers. Second, the L2R2 method sums the probabilities of all correct answers to a fixed value, when assuming H 1 When the probability of being the answer increases, the sum of the probabilities of other hypotheses will also decrease, which ...

Embodiment 2

[0093] According to the abductive inference method based on the cross focus loss of the pre-training model described in embodiment 1, the event relationship classification based on clue mining is applied, and the specific facts are as follows:

[0094] Event relationship detection is a natural language processing technology that deeply determines the correlation between two events and what kind of logical relationship they have. Its core task is to use the event as the basic semantic unit, and realize the identification and judgment of the logical relationship between events by analyzing the semantic correlation features between relationship type) two research tasks. Event relationship recognition is mainly to judge whether there is a logical or semantic relationship between two events, and pre-collecting samples for deep relationship detection between events is an important prerequisite for in-depth analysis of event logic relationships; event relationship judgment is in the ...

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Abstract

The invention provides a pretraining model-based cross focus loss tracing reasoning method, and the method comprises the following steps: combining observation pairs O1 and O2 with all hypotheses to obtain an input sequence; inputting a single input variable in the input sequence into the pre-training model to obtain a feature matrix corresponding to a sentence level, and then summing word dimensions of the feature matrix to obtain a feature vector; traversing all single input variables in the input sequence to obtain a feature vector sequence; inputting the feature vector sequence into a bidirectional long short-term memory network to obtain distributed feature representation, and performing mapping summation by using a full connection layer to obtain a score of each input; forming N groups by N values with true labels in the input sequence and all values with false labels, and performing intra-group softmax to obtain a cross prediction value; introducing a clustering factor and a weight factor, and improving FocalLoss to obtain a training loss function; and optimizing the training loss function to obtain an optimal cause-tracing 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...

Claims

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Application Information

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IPC IPC(8): G06N5/04G06N3/04
CPCG06N5/04G06N3/044
Inventor陶建华徐铭杨国花张大伟刘通
OwnerINST OF AUTOMATION CHINESE ACAD OF SCI