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.

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 the Russian language, with its rich morphology, lemmatizing the training and testing data for ELMo representations yields small but consistent improvements in the WSD task.  
 Question: What other examples of morphologically-rich languages do the authors give?
Russian

We first test our model on the single domain dataset, WoZ2.0 BIBREF19 . It consists of 1,200 dialogues from the restaurant reservation domain with three pre-defined slots: food, price range, and area. Since the name slot rarely occurs in the dataset, it is not included in our experiments, following previous literature BIBREF3 , BIBREF20 . Our model is also tested on the multi-domain dataset, MultiWoZ BIBREF9 . It has a more complex ontology with 7 domains and 25 predefined slots. Since the combined slot-value pairs representation of the belief states has to be applied for the model with $O(n)$ ITC, the total number of slots is 35.  
 Question: Which datasets are used to evaluate performance?

the single domain dataset, WoZ2.0  the multi-domain dataset, MultiWoZ