\section{Introduction}

High quality writing, anything from descriptive image captioning [] to storytelling [], is an attractive task for crowdsourcing due it's ubiquitous demand in contexts such as journalism [], editing [], and natural language processing (NLP) systems []. Yet non trivial writing is a complex and cognitively demanding task, quite different from the often simple and independent tasks common to crowdsourcing [].  Recent work has addressed the issue of crowdsourcing complex tasks such as writing by breaking down the task into smaller subtasks within a framework or workflow that combines these small contributions into a greater whole []. For example, Soylent, a word processing system that provides crowdsourced editing and writing, fits crowdworkers' contributions into a framework of finding, fixing, and verifying writing tasks []. However, current approaches are still plagued by low quality individual contributions (in Soylent, for example, almost a third of contributions for some tasks were of too low quality to be useful [check]), requiring extensive quality control or templates []. These methods, although effective at controlling for the quality of writing, are either costly, make scaling much more difficult, or force standardized, stilted, writing, which is ill-suited for any writing aimed at engaging an audience.
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\indent Most crowdsourced writing has relied on financial compensation to incentivize workers []. However, crowdworkers are paid based on how many tasks they complete, not necessarily the quality of their contribution [], thus encouraging workers to complete writing tasks quickly, rather than well, possibly leading to the quality issues common in crowdsourced writing. However, not all crowdsourcing relies on financial motivation. Volunteer-based crowdsourcing, as seen in citizen science projects [] and some online studies (e.g. LITW) [], incentivizes workers with intrinsic motivations, such as a desire to support science or learn more about themselves, to complete tasks. Compared to compensated crowdsourcing, volunteer-based crowdsourcing is often more successful at tasks that require effortful, but unverifiable contributions from workers, since there is no financial incentive to complete tasks quickly []. The absence of any cost for running volunteer-based crowdsourcing also allows for sustainable and scalable contributions.

% A notable success of volunteer-based crowdsourcing in the context of writing is Wikipedia [], which has been shown to contain articles in comparable quality to professional encyclopedias []. However, Wikipedia does not guide users to write about any particular topic, leaving no opportunity for a researcher to control or direct the collection of scalable, high-quality writing.
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\indent In this paper we present a new methodology for sustainably crowdsourcing one type of high quality writing: collecting naturalistic stories grounded in visual prompts. We verify our method by comparing our collected stories with stories gathered from a paid crowdsourcing task on Amazon Mechanical Turk. We found... We discuss applications of our methodology to other writing-intensive crowdsourcing tasks and demonstrate potential uses of the our collected dataset, including Natural Language Generation (NLG) and Inference (NLI), and computational sociolinguistic analysis. 

\ta{Mention earlier that collecting the dataset is the important part}
\ta{Missing: Add in/prove cognitive effort in writing. Back up sentence length vs. cognitive effort, how is wirting and cognitive effort. Check out ROC story RW}

% We voutline our method in the context of collecting naturalistic stories grounded in visual prompts, leading to the creation of the [BLANK] dataset. 

% We compare our collected stories to stories collected in the same method using amazon mechanical turk. 

% The BLANK dataset is a collection of visually grounded, naturalistic stories with rich author and prompt metadata.










