instruction:
In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
question:
We first describe our models individually and then the ensembling technique that we employ. In the following, MN denotes Memory Networks to encode conversational history, RCNN signify R-CNN for object level representations of an image, Wt represents additional linear layer in the decoder, and LF a late fusion mechanism as defined in BIBREF0.

Models ::: LF-RCNN
Late fusion encoder BIBREF0 with concatenated history. We use two-layered LSTMs with 512 hidden units for embedding questions and history. The object-level features are weighed using only question embeddings. The word embeddings from Glove vectors are frozen and are not fine-tuned. Figure FIGREF6 gives an overview of the architecture.

Models ::: MN-RCNN
Memory network encoder BIBREF0 with bi-directional GRUs and word embeddings fine-tuned. Object-level features are weighed by question and caption embedding. The rest of the scheme is same as above. (Figure FIGREF6)

Models ::: MN-RCNN-Wt
Same as above but with an additional linear layer applied to the dot product of candidate answer and encoder output, and gated using tanh function. Compare Figure FIGREF6 with Figure FIGREF6
answer:
Which three discriminative models did they use?


question:
We manually reviewed 1,177 pairs of entities and referring expressions generated by the system. Overall, 73.3% of the non-first mentions of entities were replaced with suitable shorter and more fluent expressions.
answer:
How is fluency of generated text evaluated?


question:
A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 
answer:

How do the authors measure how distinctive a community is?