This paper surveys 60 English Machine Reading Comprehension datasets, with a view to providing a convenient resource for other researchers interested in this problem.We categorize the datasets according to their question and answer form and compare them across various dimensions including size, vocabulary, data source, method of creation, human performance level, and first question word.Our analysis reveals that Wikipedia is by far the most common data source and that there is a relative lack of why, when, and where questions across datasets.

We show that a neural approach to the task of non-factoid answer reranking can benefit from the inclusion of tried-and-tested handcrafted features.We present a novel neural network architecture based on a combination of recurrent neural networks that are used to encode questions and answers, and a multilayer perceptron.We show how this approach can be combined with additional features, in particular, the discourse features presented by Jansen et al. (2014).Our neural approach achieves state-of-the-art performance on a public dataset from Yahoo! Answers and its performance is further improved by incorporating the discourse features.Additionally, we present a new dataset of Ask Ubuntu questions where the hybrid approach also achieves good results.