IEKM: A Model Incorporating External Keyword Matrices
Cheng Luo Qin Li Zhao Yan Mengliang Rao Yunbo Cao
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.