In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: The previously mentioned datasets are all in English 
 Question: For which languages most of the existing MRC datasets are created?
[A]: English


[Q]: We assess our dataset using traditional and deep learning methods. Our simplest model is a linear SVM trained on word unigrams. SVMs have produced state-of-the-art results for many text classification tasks BIBREF13 . We also train a bidirectional Long Short-Term-Memory (BiLSTM) model, which we adapted from the sentiment analysis system of sentimentSystem,rasooli2018cross and altered to predict offensive labels instead. It consists of (1) an input embedding layer, (2) a bidirectional LSTM layer, (3) an average pooling layer of input features. The concatenation of the LSTM's and average pool layer is passed through a dense layer and the output is passed through a softmax function. We set two input channels for the input embedding layers: pre-trained FastText embeddings BIBREF14 , as well as updatable embeddings learned by the model during training. Finally, we also apply a Convolutional Neural Network (CNN) model based on the architecture of BIBREF15 , using the same multi-channel inputs as the above BiLSTM. 
 Question: What models are used in the experiment?
[A]: linear SVM bidirectional Long Short-Term-Memory (BiLSTM) Convolutional Neural Network (CNN)


[Q]: In case of polysemous words, only the first word sense (usually the most common) is taken into account. 
 Question: How do they handle polysemous words in their entity library?
[A]:
only the first word sense (usually the most common) is taken into account