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.

Example Input: To compare our results, we use the provided baseline, which is a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs.  
 Question: What was the baseline model?
Example Output: a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs

Example Input: These sentiment scores were automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15 and pretrained on in-house psychiatric EHR text. 
 Question: How do they extract topics?
Example Output:  automatically obtained through the topic extraction and sentiment analysis pipeline introduced in our prior work BIBREF15

Example Input: 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?
Example Output:
SVM MLP FastText CNN BERT Google's DialogFlow Rasa NLU