Since our goal was to provide the classifier with more context, we chose to use an architecture based on recurrent neural networks, which are able to deal with relatively large contexts. Our assumption here is that, for a good classification, we need context from both before and after the target word itself, as markers for direct speech can be found at the beginning or the end of the direct speech. We thus designed a two-branch network, visualised in Figure 1. This network receives as input a text-segment, specifically the target word in its context. The words of the input are then passed through an embedding layer and split into two parts, where the first part contains the context up to the target word and the second part contains the context following the target word. The target word itself is contained in both parts. Each part is passed through three separate LSTM-layers. In the future-branch, the context is passed through the layers in reverse, so that the target word is the last word to be read in both branches. The LSTM-layers in the past-branch are stateful and can therefore theoretically retain the entire context of the novel up to the target word. The outputs of the final LSTM-layer of both branches are concatenated. The final prediction is made based on this concatenation by a fully connected layer.