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应用科学学报 >

2024 , Vol. 42 >Issue 1: 161 - 173

DOI: https://doi.org/10.3969/j.issn.0255-8297.2024.01.013

计算机应用专辑

面向小样本抽取式问答的多标签语义校准方法

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  • 1. 贵州大学公共大数据国家重点实验室, 贵州 贵阳 550025;
    2. 贵州大学文本计算与认知智能教育部工程研究中心, 贵州 贵阳 550025

收稿日期: 2023-06-29

  网络出版日期: 2024-02-02

基金资助

国家自然科学基金（No. 62166007）资助

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

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  • 1. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou, China;
    2. Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, Guizhou University, Guiyang 550025, Guizhou, China

Received date: 2023-06-29

  Online published: 2024-02-02

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摘要

小样本抽取式问答任务旨在利用文章给定的上下文片段，抽取出真实的答案片段。其基线模型采用的方法只针对跨度进行学习，缺乏对全局语义信息的利用，在含有多组不同重复跨度的实例中存在着理解偏差等问题。为了解决上述问题，该文利用不同层级的语义提出了一种面向小样本抽取式问答任务的多标签语义校准方法。采用包含全局语义信息的头标签和基线模型中的特殊字符构成多标签进行语义融合，并利用语义融合门来控制全局信息流的引入，将全局语义信息融合到特殊字符的语义信息中。然后，利用语义筛选门对新融入的全局语义信息和该特殊字符的原有语义信息进行保留与更替，实现对标签偏差语义的校准。在8个小样本抽取式问答数据集中的56组实验结果表明：该方法在评价指标F1值上均明显优于基线模型，证明了所提方法的有效性和先进性。

关键词： 小样本抽取式问答; 跨度抽取式问答; 多标签语义融合; 双门控机制; 机器阅读理解

本文引用格式

刘青, 陈艳平, 邹安琪, 秦永彬, 黄瑞章 . 面向小样本抽取式问答的多标签语义校准方法[J]. 应用科学学报, 2024 , 42(1) : 161 -173 . DOI: 10.3969/j.issn.0255-8297.2024.01.013

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

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