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.
--------
Question: The datasets used for training, validation and testing contain sentences extracted from the Europarl corpus BIBREF1 and SoNaR corpus BIBREF2. The Europarl corpus is an open-source parallel corpus containing proceedings of the European Parliament. The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SoNaR corpus comprises two corpora: SONAR500 and SONAR1. The SONAR500 corpus consists of more than 500 million words obtained from different domains.
Question: What are the sizes of both datasets?

Answer: The Dutch section consists of 2,333,816 sentences and 53,487,257 words. The SONAR500 corpus consists of more than 500 million words obtained from different domains.

Question: word embedding, input encoder, alignment, aggregation, and prediction
Question: what is the architecture of the baseline model?

Answer: word embedding, input encoder, alignment, aggregation, and prediction.

Question: Car-speak is abstract language that pertains to a car's physical attribute(s). In this instance the physical attributes that the term "fast" pertains to could be the horsepower, or it could be the car's form factor (how the car looks). However, we do not know exactly which attributes the term "fast" refers to. We train a series of classifiers in order to classify car-speak. We train three classifiers on the review vectors that we prepared in Section SECREF8. The classifiers we use are K Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) BIBREF13.
Question: Is car-speak language collection of abstract features that classifier is later trained on?

Answer:
No