A customer service platform system with a core text semantic similarity (STS) task faces two urgent challenges: Firstly, one platform system needs to adapt to different domains of customers, i.e., different domains adaptation (DDA). Secondly, it is difficult for the model of the platform system to distinguish sentence pairs that are literally close but semantically different, i.e., hard negative samples. In this paper, we propose an incorporation external keywords matrices model (IEKM) to address these challenges. The model uses external tools or dictionaries to construct external matrices and fuses them to the self-attention layers of the Transformer structure through gating units, thus enabling flexible corrections to the model results. We evaluate the method on multiple datasets and the results show that our method has improved performance on all datasets. To demonstrate that our method can effectively solve all the above challenges, we conduct a flexible correction experiment, which results in an increase in the F1 value from 56.61 to 73.53. Our code will be publicly available.

提出了一种使用外部关键词矩阵模型（IEKM）解决客户服务平台系统面对的不同领域适应和语义上相似但句子对在字面上接近而语义上不同的困难负样例的挑战，在Transformer结构的自注意力层中通过门单元融合外部工具或字典构建的外部矩阵，实现对模型结果的灵活校正，实验结果表明，该方法在多个数据集上表现出改进性能，F1值从56.61提高至73.53。

IEKM：一个集成外部关键词矩阵的模型