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.

[Q]: We propose to derive rewards from human-human dialogues by assigning positive values to contextualised responses seen in the data, and negative values to randomly chosen responses due to lacking coherence (also referred to as `non-human-like responses') – see example in Tables TABREF29 and TABREF30 . 
 Question: How do they obtain human generated policies?
[A]: derive rewards from human-human dialogues by assigning positive values to contextualised responses seen in the data, and negative values to randomly chosen responses due to lacking coherence


[Q]: 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?
[A]: Step-Wise Decoder Fusion Multimodal Attention Modulation Visual-Semantic (VS) Regularizer


[Q]: It is an order of magnitude more efficient in terms of training time. The model is complex, both in terms of implementation and run-time. Indeed, this model requires pre-training and mutual-learning and requires days of training time, whereas the simple architecture we propose requires on the order of an hour (and is easy to implement). MGNC-CNN is usually better than MG-CNN. And on the Subj dataset, MG-CNN actually achieves slightly better results than BIBREF11 , with far less complexity and required training time (MGNC-CNN performs comparably, although no better, here). 
 Question: How much faster is training time for MGNC-CNN over the baselines?
[A]:
It is an order of magnitude more efficient in terms of training time. his model requires pre-training and mutual-learning and requires days of training time, whereas the simple architecture we propose requires on the order of an hour