提出一种双向注意力文本关键词匹配的法条推荐模型(BiAKLaw).该模型以预训练语言模型 BERT 作为基础匹配模型,利用双向注意力机制提取字符级对齐特征和关键词差异特征,融合对齐特征、差异特征和关键词语义表征来提升匹配效果.在裁判文书交通肇事和故意伤害数据集上的实验结果表明,与 BERT 模型相比,BiAKLaw 在评价指标 F1 上分别提升 3.74%和 3.43%.
Bi-Attention Text-Keyword Matching for Law Recommendation
This paper proposed a bi-directional attention based text-keyword matching model for law recommen-dation(BiAKLaw).In this model,BERT is utilized as a basic matching model,bi-directional attention mechanism is implemented to extract token-level alignment features and keyword-level differential features,and these features are fused with keyword attentive semantic representations for a better matching effect.The experimental results on the traffic accident and intentional injury datasets demonstrate that,compared with BERT,the proposed model increases F1 evaluation metric by 3.74%and 3.43%respectively.
law recommendationcase facttext matchingattention mechanism