融入本体信息的开放规则生成

ZHOU Shiqi,BI Sheng, ZHANG Jiatao,GAO Huan

Journal of Shanxi University(Natural Science Edition)（2023）

Cited 0|Views14

Abstract

规则具有表达能力强、易于解释、应用广泛的特点.传统的封闭规则生成方法依赖于预定义的实体和关系,而现有的开放规则生成方法忽略实例类型作为规则生成的限制条件,导致生成不合理的规则.为解决上述问题,本文提出一种融入本体信息进行开放规则生成的方法.提出的方法将实例类别与实例之间隐藏的逻辑层级关系信息引入生成过程,并采用规则多样解码方式,以提升规则生成的正确率与多样性.在开放规则数据集Open-Rule155上,该方法较之prompt、Comet、Orion模型,在生成规则的质量与多样性指标上有明显的提升,较之次优的Orion方法,BLEU-1、ROUGE-L指标提升在3个百分点以上,BLEU-2、BLEU-4指标提升接近6个百分点.同时,消融实验表明,该方法性能的提升主要来源于本体信息的约束,更优的解码策略也有助于生成更多样的规则.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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