Figure 1: An example screenshot of our system for the topic 'co-creative systems' in the discipline 'human-computer interaction'. The system has generated three "sparks": sentences intended to inspire the participant when writing an explanation for their topic. The frst spark has been marked as inspirational.
People are increasingly getting information and news from social media. On Twitter we are seeing the emergence of "tweetorials" -- long, explanatory Twitter threads written by experts. In this work we study tweetorials as a form of science writing. While scientists have begun to champion the importance of Twitter as a science communication medium, few have studied how people are successfully using this medium to communicate complex and nuanced ideas. To understand how tweetorials work, we curated a collection of 46 clear and engaging tweetorials from multiple domains. We analyzed these tweetorials for the writing techniques that they employ, and found that while tweetorials use many traditional science writing techniques, they also use more subjective language, actively build credibility, and incorporate media in unique ways. In addition, we report on a workshop we ran to aid science PhD students in writing tweetorials, and find that while providing common tweetorial techniques improves their writing, the students still struggle to balance their scientific sensibilities with the informal tone associated with tweetorials. We discuss the implications of using informal and subjective language in science communication, as well as how technology can support scientists in writing tweetorials.
Despite the success of style transfer in image processing, it has seen limited progress in natural language generation. Part of the problem is that content is not as easily decoupled from style in the text domain. Curiously, in the field of stylometry, content does not figure prominently in practical methods of discriminating stylistic elements, such as authorship and genre. Rather, syntax and function words are the most salient features. Drawing on this work, we model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions. We train a neural encoder-decoder model to reconstruct reference sentences given only content words and the setting of the controls. We perform style transfer by keeping the content words fixed while adjusting the controls to be indicative of another style. In experiments, we show that the model reliably responds to the linguistic controls and perform both automatic and manual evaluations on style transfer. We find we can fool a style classifier 84% of the time, and that our model produces highly diverse and stylistically distinctive outputs. This work introduces a formal, extendable model of style that can add control to any neural text generation system.
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