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: Then we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0 We note that the similarity metric defined above for sentences is equally applicable for n-gram retrieval. 
 Question: Which similarity measure do they use in their n-gram retrieval approach?
Example Output: we define the similarity between INLINEFORM7 and INLINEFORM8 by, DISPLAYFORM0

Example Input: he first dataset (WIKI) was the same set of 200 sentences from Wikipedia used in BIBREF7 .  The second dataset (SCI) was a set of 220 sentences from the scientific literature. We sourced the sentences from the OA-STM corpus. In total 2247 triples were extracted. In total, 11262 judgements were obtained after running the annotation process. 
 Question: What is the size of the released dataset?
Example Output: 440 sentences, 2247 triples extracted from those sentences, and 11262 judgements on those triples.

Example Input:  In contrast to most encoder-decoder architectures, which are purely based on recurrent neural network (RNNs) framework, we construct an encoder with several convolutional layers BIBREF14 followed by NIN layers BIBREF15 as the lower part in the encoder and integrate them with deep bidirectional long short-term memory (Bi-LSTM) BIBREF16 at the higher part. On the decoder side, we use a standard deep unidirectional LSTM with global attention BIBREF13 that is calculated by a multi-layer perceptron (MLP) as described in Eq. EQREF2 . 
 Question: Which architecture do they use for the encoder and decoder?
Example Output:
we construct an encoder with several convolutional layers BIBREF14 followed by NIN layers BIBREF15 as the lower part in the encoder and integrate them with deep bidirectional long short-term memory (Bi-LSTM) BIBREF16 at the higher part On the decoder side, we use a standard deep unidirectional LSTM with global attention BIBREF13 that is calculated by a multi-layer perceptron (MLP)