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.

These workers completed 1001 tasks: 496 tasks in the control and 505 in the AUI.  
 Question: How many responses did they obtain?
1001

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?
linear SVM bidirectional Long Short-Term-Memory (BiLSTM) Convolutional Neural Network (CNN)

For instance, a message can be regarded as harmless on its own, but when taking previous threads into account it may be seen as abusive, and vice versa. This aspect makes detecting abusive language extremely laborious even for human annotators; therefore it is difficult to build a large and reliable dataset BIBREF10 . 
 Question: What examples of the difficulties presented by the context-dependent nature of online aggression do they authors give?

detecting abusive language extremely laborious it is difficult to build a large and reliable dataset