Finally, once questions are formulated using a variety of rich and sophisticated representations in natural languages, such factoid QA tasks may serve as an excellent testbed to study many natural language understanding problems, e.g., examine the recently emerging research efforts to combine neural models with the traditional symbolic processing methods (Liang et al., 2017;Mou et al., 2017).
machine learning approaches for NLP are data hungry since they require large amounts of real-world data to train the models for the best possible performance.