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 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?

Ex Output:
a non-parameter optimized linear-kernel SVM that uses TF-IDF bag-of-word vectors as inputs

Ex Input:
To account for sequences of words and characters that might carry useful information, we extracted word unigrams, bigrams, and trigrams as features.
Question: Which features do they use to model Twitter messages?

Ex Output:
word unigrams, bigrams, and trigrams

Ex Input:
The encoder is a Convolutional Neural Network (CNN) and the decoder is a Long Short-Term Memory (LSTM) network, as presented in Figure 2 . The image is passed through the encoder generating the image representation that is used by the decoder to know the content of the image and generate the description word by word.
Question: What model is used to encode the images?

Ex Output:
a Convolutional Neural Network (CNN)