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: The final embedding dimensionality is equal to the number of unique word labels in the training set, which is 1061.
Question: Which dimensionality do they use for their embeddings?
Example Output: 1061

Example Input: We compare our model with two sets of baselines:

MemN2N BIBREF12 is an end-to-end trainable version of memory networks BIBREF9 . Attentive and Impatient Readers BIBREF6 use bidirectional LSTMs to encode question and evidence, and do classification over a large vocabulary based on these two encodings.
Question: What are the baselines?
Example Output: MemN2N BIBREF12 Attentive and Impatient Readers BIBREF6

Example Input: As a document classifier we employ a word-based CNN similar to Kim consisting of the following sequence of layers: $ \texttt {Conv} \xrightarrow{} \texttt {ReLU} \xrightarrow{} \texttt {1-Max-Pool} \xrightarrow{} \texttt {FC} \\ $ Future work would include applying LRP to other neural network architectures (e.g. character-based or recurrent models) on further NLP tasks, as well as exploring how relevance information could be taken into account to improve the classifier's training procedure or prediction performance.
Question: Do the experiments explore how various architectures and layers contribute towards certain decisions?
Example Output:
No