You will be shown sections from a scientific research paper, together with a question about the paper. Paragraphs in the paper are separated by newlines. Your task is to answer the question based on the contents of the paper.

Paper:
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Structured Embedding Models for Grouped Data

Word embeddings are a powerful approach for analyzing language, and exponential family embeddings (EFE) extend them to other types of data. Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data. We study how the word usage of U.S. Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons. Key to the success of our method is that the groups share statistical information. We develop two sharing strategies: hierarchical modeling and amortization. We demonstrate the benefits of this approach in empirical studies of speeches, abstracts, and shopping baskets. We show how S-EFE enables group-specific interpretation of word usage, and outperforms EFE in predicting held-out data.

Introduction
Word embeddings BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 are unsupervised learning methods for capturing latent semantic structure in language. Word embedding methods analyze text data to learn distributed representations of the vocabulary that capture its co-occurrence statistics. These representations are useful for reasoning about word usage and meaning BIBREF7 , BIBREF8 . Word embeddings have also been extended to data beyond text BIBREF9 , BIBREF10 , such as items in a grocery store or neurons in the brain. efe is a probabilistic perspective on embeddings that encompasses many existing methods and opens the door to bringing expressive probabilistic modeling BIBREF11 , BIBREF12 to the problem of learning distributed representations.
We develop sefe, an extension of efe for studying how embeddings can vary across groups of related data. We will study several examples: in U.S. Congressional speeches, word usage can vary across states or party affiliations; in scientific literature, the usage patterns of technical terms can vary across fields; in supermarket shopping data, co-purchase patterns of items can vary across seasons of the year. We will see that sefe discovers a per-group embedding representation of objects. While the naïve approach of fitting an individual embedding model for each group would typically suffer from lack of data—especially in groups for which fewer observations are available—we develop two methods that can share information across groups.
Figure FIGREF1 illustrates the kind of variation that we can capture. We fit an sefe to ArXiv abstracts grouped into different sections, such as computer science (cs), quantitative finance (q-fin), and nonlinear sciences (nlin). sefe results in a per-section embedding of each term in the vocabulary. Using the fitted embeddings, we illustrate similar words to the word 1.10intelligence. We can see that how 1.10intelligence is used varies by field: in computer science the most similar words include 1.10artificial and 1.10ai; in finance, similar words include 1.10abilities and 1.10consciousness.
In more detail, embedding methods posit two representation vectors for each term in the vocabulary; an embedding vector and a context vector. (We use the language of text for concreteness; as we mentioned, efe extend to other types of data.) The idea is that the conditional probability of each observed word depends on the interaction between the embedding vector and the context vectors of the surrounding words. In sefe, we posit a separate set of embedding vectors for each group but a shared set of context vectors; this ensures that the embedding vectors are in the same space.
We propose two methods to share statistical strength among the embedding vectors. The first approach is based on hierarchical modeling BIBREF13 , which assumes that the group-specific embedding representations are tied through a global embedding. The second approach is based on amortization BIBREF14 , BIBREF15 , which considers that the individual embeddings are the output of a deterministic function of a global embedding representation. We use stochastic optimization to fit large data sets.
Our work relates closely to two threads of research in the embedding literature. One is embedding methods that study how language evolves over time BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 . Time can be thought of as a type of “group”, though with evolutionary structure that we do not consider. The second thread is multilingual embeddings BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 ; our approach is different in that most words appear in all groups and we are interested in the variations of the embeddings across those groups.
Our contributions are thus as follows. We introduce the sefe model, extending efe to grouped data. We present two techniques to share statistical strength among the embedding vectors, one based on hierarchical modeling and one based on amortization. We carry out a thorough experimental study on two text databases, ArXiv papers by section and U.S. Congressional speeches by home state and political party. Using Poisson embeddings, we study market basket data from a large grocery store, grouped by season. On all three data sets, sefe outperforms efe in terms of held-out log-likelihood. Qualitatively, we demonstrate how sefe discovers which words are used most differently across U.S. states and political parties, and show how word usage changes in different ArXiv disciplines.

Empirical Study
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:

Table 3: List of the three most different words for different groups for the Congressional speeches. S-EFE uncovers which words are used most differently by Republican Senators (red) and Democratic Senators (blue) from different states. The complete table is in the Appendix.
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Question: What experiments are used to demonstrate the benefits of this approach?

To answer the question, format your response as a `json` object with two fields:

"answer": A string providing a succinct answer to the question, in your own words.
"evidence": An array of strings. Each entry should be a full paragraph from the paper. Together, the evidence should serve as a justification for the answer.

For instance, for the question "What baselines did the authors compare against?", a sample response might be:

{
  "answer": "BERT and RoBERTa."
  "evidence": ["We compare our approach against two baselines. In Table 1, we compare against BERT. In Table 2, we compare against RoBERTa. Our findings indicate that our approach improves over both baeslines..."]
}

The "answer" field should be roughly 62 characters in length.

Do not include any text in your response other than the json. If the question is unanswerable given the provided excerpts, respond with the single word "null".

To repeat, the question is: What experiments are used to demonstrate the benefits of this approach?

Answer JSON object:
{"answer": "Calculate test log-likelihood on the three considered datasets", "evidence": ["Our contributions are thus as follows. We introduce the sefe model, extending efe to grouped data. We present two techniques to share statistical strength among the embedding vectors, one based on hierarchical modeling and one based on amortization. We carry out a thorough experimental study on two text databases, ArXiv papers by section and U.S. Congressional speeches by home state and political party. Using Poisson embeddings, we study market basket data from a large grocery store, grouped by season. On all three data sets, sefe outperforms efe in terms of held-out log-likelihood. Qualitatively, we demonstrate how sefe discovers which words are used most differently across U.S. states and political parties, and show how word usage changes in different ArXiv disciplines."]}