MARKS-mech: A Mask-based Prior Knowledge Dissemination Mechanism for including Discourse Relations for Sentiment Classification

Disseminating prior knowledge about a pattern recognition task in Deep Neural Networks (DNNs) is desirable, to enable them to learn some complex patterns or representations, that are otherwise difficult to learn via usual data-driven training. Several methods have been proposed for that purpose, but creating an end-to-end trainable DNN model, while keeping it informed with prior knowledge, remains a challenging task. In this paper, we propose a method to disseminate prior knowledge in DNN models. Specifically, we created a novel MAsk-based pRior Knowledge diSsemination mechanism (MARKS-mech), that transfers logical prior knowledge in DNN models via input data transformation. We utilize a recently constructed Twitter-based dataset to perform our experiments, which is specifically designed to test the logical prior knowledge dissemination ability of methods like ours. We find that our method provides superior knowledge dissemination performance compared to the baselines.