A Deep Learning Model of Common Sense Knowledge for Augmenting Natural Language Processing Tasks in Portuguese Language

Despite the richness and vastness of the common-sense knowledge bases, we argue that common-sense knowledge has to be integrated into the target applications (Text Classification, Dialogue systems, Information Extraction systems, etc.) more effectively. In order to consider this common-sense knowledge in target applications, we propose a deep learning model of common-sense knowledge in Portuguese language, which can be easily coupled in Natural Language Understanding (NLU) systems in order to leverage their performance. More specifically, the model is composed by a neural network LSTM (Long Short Term Memory) that receives a text from the target application, for example, an user message in a dialog, a response to a user tweet, a news text; and selects and learns what is the best set of common-sense relations to return to the target application, which should be considered in the target learning model or system. We implemented the common-sense learning module in two target applications - a Stance Classification system and an End-to-End Dialogue system. In both cases, incorporating the deep learning model improved the results.