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.

Ex Input:
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

Ex Output:
Which three discriminative models did they use?


Ex Input:
(Information Retrieval). This baseline has been successfully used for related tasks like Question Answering BIBREF39 . We create two versions of this baseline: one with the pool of perspectives INLINEFORM0 and one with the pool of evidences INLINEFORM1 .

Ex Output:
Which machine baselines are used?


Ex Input:
To build the classifiers we used three different learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and Support Vector Machines (SVM).

Ex Output:

Show your work and conclude with the exact phrasing "Therefore, the final answer is [answer]." where [answer] is just the final number or expression that solves the problem.
user
Which supervised learning algorithms are used in the experiments?


