Question: What are the user-defined keywords?

Evidence:
raw_evidence: Nonetheless, GANs have continued to show their value particularly in the domain of text-generation. Of particular interest for our purposes, Radford et al. propose synthesizing images from text descriptions [3]. The group demonstrates how GANs can produce images that correspond to a user-defined text description. It thus seems feasible that by using a similar model, we can produce text samples that are conditioned upon a set of user-specified keywords.
where x is defined as the predicted label for each sample and y is the true label (i.e. real versus fake data). The DMK loss then calculates an additional term, which corresponds to the dot-product attention of each word in the generated output with each keyword specified by the user. To illustrate by example, say a user desires the generated output to contain the keywords, $\lbrace subway, manhattan\rbrace $ . The model then converts each of these keywords to their corresponding glove vectors. Let us define the following notation $e(‘apple’)$ is the GloVe representation of the word apple, and let us suppose that $g$ is the vector of word embeddings generated by the generator. That is, $g_1$ is the first word embedding of the generator’s output. Let us also suppose $k$ is a vector of the keywords specified by the user. In our examples, $k$ is always in $R^{1}$ with $k_1$ one equaling of $‘subway’$ or $‘parking’$ . The dot-product term of the DMK loss then calculates $e(‘apple’)$0 . Weighing this term by some hyper-parameter, $e(‘apple’)$1 , then gives us the entire definition of the DMK loss: $e(‘apple’)$2 $e(‘apple’)$3
raw_evidence: The data for the project was acquired from Airdna, a data processing service that collaborates with Airbnb to produce high-accuracy data summaries for listings in geographic regions of the United States. For the sake of simplicity, we focus our analysis on Airbnb listings from Manhattan, NY, during the time period of January 1, 2016, to January 1, 2017. The data provided to us contained information for roughly 40,000 Manhattan listings that were posted on Airbnb during this defined time period. For each listing, we were given information of the amenities of the listing (number of bathrooms, number of bedrooms …), the listing’s zip code, the host’s description of the listing, the price of the listing, and the occupancy rate of the listing. Airbnb defines a home's occupancy rate, as the percentage of time that a listing is occupied over the time period that the listing is available. This gives us a reasonable metric for defining popular versus less popular listings.
where x is defined as the predicted label for each sample and y is the true label (i.e. real versus fake data). The DMK loss then calculates an additional term, which corresponds to the dot-product attention of each word in the generated output with each keyword specified by the user. To illustrate by example, say a user desires the generated output to contain the keywords, $\lbrace subway, manhattan\rbrace $ . The model then converts each of these keywords to their corresponding glove vectors. Let us define the following notation $e(‘apple’)$ is the GloVe representation of the word apple, and let us suppose that $g$ is the vector of word embeddings generated by the generator. That is, $g_1$ is the first word embedding of the generator’s output. Let us also suppose $k$ is a vector of the keywords specified by the user. In our examples, $k$ is always in $R^{1}$ with $k_1$ one equaling of $‘subway’$ or $‘parking’$ . The dot-product term of the DMK loss then calculates $e(‘apple’)$0 . Weighing this term by some hyper-parameter, $e(‘apple’)$1 , then gives us the entire definition of the DMK loss: $e(‘apple’)$2 $e(‘apple’)$3

Answers:
Words that a user wants them to appear in the generated output.
terms common to hosts' descriptions of popular Airbnb properties, like 'subway', 'manhattan', or 'parking'