Modern deep learning algorithms often do away with feature engineering and learn latent representations directly from raw data that are given as input to Deep Neural Networks (DNNs) McCann et al., 2017;Peters et al., 2018).
it has been shown that linguistic knowledge (manually or semi-automatically encoded into lexicons and knowledge bases) can significantly improve DNN performance for Natural Language Processing (NLP) tasks, such as natural language inference (Mrkšić et al., 2017), language modelling (Ahn et al., 2016), named entity recognition (Ghaddar and Langlais, 2018) and relation extraction (Vashishth et al., 2018).