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 Q]: Experiments ::: Baselines
We evaluate MPAD against multiple state-of-the-art baseline models, including hierarchical ones, to enable fair comparison with the hierarchical MPAD variants.

doc2vec BIBREF37. Doc2vec (or paragraph vector) is an extension of word2vec that learns vectors for documents in a fully unsupervised manner. Document embeddings are then fed to a logistic regression classifier.

CNN BIBREF38. The convolutional neural network architecture, well-known in computer vision, is applied to text. There is one spatial dimension and the word embeddings are used as channels (depth dimensions).

DAN BIBREF39. The Deep Averaging Network passes the unweighted average of the embeddings of the input words through multiple dense layers and a final softmax.

Tree-LSTM BIBREF40 is a generalization of the standard LSTM architecture to constituency and dependency parse trees.

DRNN BIBREF41. Recursive neural networks are stacked and applied to parse trees.

LSTMN BIBREF42 is an extension of the LSTM model where the memory cell is replaced by a memory network which stores word representations.

C-LSTM BIBREF43 combines convolutional and recurrent neural networks. The region embeddings provided by a CNN are fed to a LSTM.

SPGK BIBREF44 also models documents as word co-occurrence networks. It computes a graph kernel that compares shortest paths extracted from the word co-occurrence networks and then uses a SVM to categorize documents.

WMD BIBREF45 is an application of the well-known Earth Mover's Distance to text. A k-nearest neighbor classifier is used.

S-WMD BIBREF46 is a supervised extension of the Word Mover's Distance.

Semantic-CNN BIBREF47. Here, a CNN is applied to semantic units obtained by clustering words in the embedding space.

LSTM-GRNN BIBREF26 is a hierarchical model where sentence embeddings are obtained with a CNN and a GRU-RNN is fed the sentence representations to obtain a document vector.

HN-ATT BIBREF27 is another hierarchical model, where the same encoder architecture (a bidirectional GRU-RNN) is used for both sentences and documents, with different parameters. A self-attention mechanism is applied to the RNN annotations at each level.
[EX A]: What is the state-of-the-art system?

[EX Q]: In this section, we describe the experimental study. We fit the sefe model on three datasets and compare it against the efe BIBREF10 . Our quantitative results show that sharing the context vectors provides better results, and that amortization and hierarchical structure give further improvements. Data. We apply the sefe on three datasets: ArXiv papers, U.S. Senate speeches, and purchases on supermarket grocery shopping data. We describe these datasets below, and we provide a summary of the datasets in Table TABREF17 . ArXiv papers: This dataset contains the abstracts of papers published on the ArXiv under the 19 different tags between April 2007 and June 2015. We treat each tag as a group and fit sefe with the goal of uncovering which words have the strongest shift in usage. We split the abstracts into training, validation, and test sets, with proportions of INLINEFORM0 , INLINEFORM1 , and INLINEFORM2 , respectively.

Senate speeches: This dataset contains U.S. Senate speeches from 1994 to mid 2009. In contrast to the ArXiv collection, it is a transcript of spoken language. We group the data into state of origin of the speaker and his or her party affiliation. Only affiliations with the Republican and Democratic Party are considered. As a result, there are 83 groups (Republicans from Alabama, Democrats from Alabama, Republicans from Arkansas, etc.). Some of the state/party combinations are not available in the data, as some of the 50 states have only had Senators with the same party affiliation. We split the speeches into training ( INLINEFORM0 ), validation ( INLINEFORM1 ), and testing ( INLINEFORM2 ).

Grocery shopping data: This dataset contains the purchases of INLINEFORM0 customers. The data covers a period of 97 weeks. After removing low-frequency items, the data contains INLINEFORM1 unique items at the 1.10upc (Universal Product Code) level. We split the data into a training, test, and validation sets, with proportions of INLINEFORM2 , INLINEFORM3 , and INLINEFORM4 , respectively. The training data contains INLINEFORM5 shopping trips and INLINEFORM6 purchases in total. For the text corpora, we fix the vocabulary to the 15k most frequent terms and remove all words that are not in the vocabulary. Following BIBREF2 , we additionally remove each word with probability INLINEFORM0 , where INLINEFORM1 is the word frequency. This downsamples especially the frequent words and speeds up training. (Sizes reported in Table TABREF17 are the number of words remaining after preprocessing.) Models. Our goal is to fit the sefe model on these datasets. For the text data, we use the Bernoulli distribution as the conditional exponential family, while for the shopping data we use the Poisson distribution, which is more appropriate for count data. On each dataset, we compare four approaches based on sefe with two efe BIBREF10 baselines. All are fit using sgd BIBREF34 . In particular, we compare the following methods:
[EX A]: What experiments are used to demonstrate the benefits of this approach?

[EX Q]: The basic idea of the visualization, drawing on Isaac Newton’s visualization of the color spectrum BIBREF8 , is to express a mixture in terms of its constituents as represented in barycentric coordinates. This visualization allows an intuitive interpretation of which country a recipe belongs to. If the probability of Japanese is high, the recipe is mapped near the Japanese. The countries on the Newton diagram are placed by spectral graph drawing BIBREF9 , so that similar countries are placed nearby on the circle. 
[EX A]:
What is barycentric Newton diagram?