instruction:
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.
question:
To enable this, we use a bifocal attention mechanism which computes an attention over fields at a macro level and over values at a micro level. We then fuse these attention weights such that the attention weight for a field also influences the attention over the values within it. Fused Bifocal Attention Mechanism
Intuitively, when a human writes a description from a table she keeps track of information at two levels. At the macro level, it is important to decide which is the appropriate field to attend to next and at a micro level (i.e., within a field) it is important to know which values to attend to next. To capture this behavior, we use a bifocal attention mechanism as described below. Fused Attention: Intuitively, the attention weights assigned to a field should have an influence on all the values belonging to the particular field. To ensure this, we reweigh the micro level attention weights based on the corresponding macro level attention weights. In other words, we fuse the attention weights at the two levels as: DISPLAYFORM0

where INLINEFORM0 is the field corresponding to the INLINEFORM1 -th value, INLINEFORM2 is the macro level context vector. 
 Question: What is a bifocal attention mechanism?
answer:
At the macro level, it is important to decide which is the appropriate field to attend to next micro level (i.e., within a field) it is important to know which values to attend to next fuse the attention weights at the two levels


question:
We defined the reward as being 1 for successfully completing the task, and 0 otherwise. A discount of $0.95$ was used to incentivize the system to complete dialogs faster rather than slower, yielding return 0 for failed dialogs, and $G = 0.95^{T-1}$ for successful dialogs, where $T$ is the number of system turns in the dialog.   0.95^{T-1} reward  0.95^{T-1}  
 Question: What is the reward model for the reinforcement learning appraoch?
answer:
reward 1 for successfully completing the task, with a discount by the number of turns, and reward 0 when fail


question:
We choose the following three models as the baselines:

K-means is a well known data clustering algorithm, we implement the algorithm using sklearn toolbox, and represent documents using bag-of-words weighted by TF-IDF.

LEM BIBREF13 is a Bayesian modeling approach for open-domain event extraction. It treats an event as a latent variable and models the generation of an event as a joint distribution of its individual event elements. We implement the algorithm with the default configuration.

DPEMM BIBREF14 is a non-parametric mixture model for event extraction. It addresses the limitation of LEM that the number of events should be known beforehand. We implement the model with the default configuration. 
 Question: What baseline approaches does this approach out-perform?
answer:

K-means LEM BIBREF13 DPEMM BIBREF14