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.

Input: Consider Input: Our pool of annotators is selected after several short training rounds, with up to 15 predicates per round, in which they received extensive personal feedback. 
 Question: How are workers trained?

Output: extensive personal feedback


Input: Consider 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?

Output: Step-Wise Decoder Fusion Multimodal Attention Modulation Visual-Semantic (VS) Regularizer


Input: Consider Input: Although no perfect matches exist, we decided upon the Quora duplicate question dataset BIBREF22 as the best match. To study the embeddings, we computed the euclidean distance between the two questions using various embeddings, to study the distance between semantically similar and dissimilar questions. 
 Question: What existing corpus is used for comparison in these experiments?


Output: Quora duplicate question dataset BIBREF22