  Knowledge enhanced pre-trained language models (K-PLMs) are shown to be
effective for many public tasks in the literature but few of them have been
successfully applied in practice. To address this problem, we propose K-AID, a
systematic approach that includes a low-cost knowledge acquisition process for
acquiring domain knowledge, an effective knowledge infusion module for
improving model performance, and a knowledge distillation component for
reducing the model size and deploying K-PLMs on resource-restricted devices
(e.g., CPU) for real-world application. Importantly, instead of capturing
entity knowledge like the majority of existing K-PLMs, our approach captures
relational knowledge, which contributes to better-improving sentence-level text
classification and text matching tasks that play a key role in question
answering (QA). We conducted a set of experiments on five text classification
tasks and three text matching tasks from three domains, namely E-commerce,
Government, and Film&TV, and performed online A/B tests in E-commerce.
Experimental results show that our approach is able to achieve substantial
improvement on sentence-level question answering tasks and bring beneficial
business value in industrial settings.
