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.

[EX Q]: 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?
[EX A]: Russian

[EX Q]: This action space is of the order of $A=\mathcal {O}(|V| imes |O|^2)$ where $V$ is the number of action verbs, and $O$ is the number of distinct objects in the world that the agent can interact with. As this is too large a space for a RL agent to effectively explore, the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7
Question: How is the domain knowledge transfer represented as knowledge graph?
[EX A]: the knowledge graph is used to prune this space by ranking actions based on their presence in the current knowledge graph and the relations between the objects in the graph as in BIBREF7

[EX Q]: Benchmark Evaluation ::: Classifier Models
SVM: A linear support vector machine with bag-of-words sentence representations.

MLP: A multi-layer perceptron with USE embeddings BIBREF4 as input.

FastText: A shallow neural network that averages embeddings of n-grams BIBREF5.

CNN: A convolutional neural network with non-static word embeddings initialized with GloVe BIBREF6.

BERT: A neural network that is trained to predict elided words in text and then fine-tuned on our data BIBREF1.

Platforms: Several platforms exist for the development of task-oriented agents. We consider Google's DialogFlow and Rasa NLU with spacy-sklearn.
Question: Which classifiers are evaluated?
[EX A]:
SVM MLP FastText CNN BERT Google's DialogFlow Rasa NLU