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: After Korektor, there are 4262 unique sentences (including 150 seed sentences) that form the COSTRA 1.0 dataset. 
 Question: How many sentence transformations on average are available per unique sentence in dataset?
Example Output: 27.41 transformation on average of single seed sentence is available in dataset.

Example Input: Proposed Fusion Techniques ::: Step-Wise Decoder Fusion
Our first proposed technique is the step-wise decoder fusion of visual features during every prediction step i.e. we concatenate the visual encoding as context at each step of the decoding process. Proposed Fusion Techniques ::: Multimodal Attention Modulation
Similar to general attention BIBREF8, wherein a variable-length alignment vector $a_{th}(s)$, whose size equals the number of time steps on the source side, is derived by comparing the current target hidden state $h_{t}$ with each source hidden state $\overline{h_{s}}$; we consider a variant wherein the visual encoding $v_{t}$ is used to calculate an attention distribution $a_{tv}(s)$ over the source encodings as well. Proposed Fusion Techniques ::: Visual-Semantic (VS) Regularizer
In terms of leveraging the visual modality for supervision, BIBREF1 use multi-task learning to learn grounded representations through image representation prediction. 
 Question: What are 3 novel fusion techniques that are proposed?
Example Output: Step-Wise Decoder Fusion Multimodal Attention Modulation Visual-Semantic (VS) Regularizer

Example Input:  We draw our data from news publications, wine reviews, and Reddit, which in addition to large volume, also let us characterize binomials in new ways, and analyze differences in binomial orderings across communities and over time.  
 Question: What online text resources are used to test binomial lists?
Example Output:
news publications, wine reviews, and Reddit