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.

翻译：以核心文本语义相似度任务为基础的客服平台系统面临两个紧迫挑战：首先，一个平台系统需要适应不同领域的客户，即领域自适应问题；其次，平台系统模型难以区分字面接近但语义不同的句子对，即困难负样本。本文提出融合外部关键词矩阵模型（IEKM）以应对这些挑战。该模型利用外部工具或词典构建外部矩阵，并通过门控单元将其融合至Transformer结构的自注意力层，从而实现对模型结果的灵活校正。我们在多个数据集上进行了方法评估，结果表明该方法在所有数据集上均实现了性能提升。为验证该方法能有效解决上述挑战，我们开展了灵活校正实验，实验结果显示F1值从56.61提升至73.53。我们的代码将公开。