面向小样本抽取式问答的多标签语义校准方法

doi:10.3969/j.issn.0255-8297.2024.01.013

Abstract

Abstract: biases, especially in instances involving multiple sets of distinct repeated spans. Therefore, this paper proposes a multi-label semantic calibration method for few-shot extractive QA to mitigate the above issues. Specifically, this method uses the head label, which contains global semantic information, and the special character in the baseline model to form a multi-label for semantic fusion. The semantic fusion gate is then used to control the introduction of global information flow to integrate global semantic information into the semantic information of the special character. Next, the semantic selection gate is used to retain or replace the newly integrated global semantic information and the original semantic information of the special character, achieving semantic adjustment of label bias. The results of 56 experiments on 8 few-shot extractive QA datasets consistently outperformed the baseline model in terms of the evaluation metric F1 score. This demonstrates the effectiveness and advancement of the method.

Key words: few-shot extraction question answering, span extraction question answering, multi-label semantic fusion, dual gating mechanism, machine reading comprehension

CLC Number:

P751.1

LIU Qing, CHEN Yanping, ZOU Anqi, QIN Yongbin, HUANG Ruizhang. A Multi-label Semantic Calibration Method for Few Shot Extractive Question[J]. Journal of Applied Sciences, 2024, 42(1): 161-173.

References

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A Multi-label Semantic Calibration Method for Few Shot Extractive Question

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