  Building datasets of creative text, such as humor, is quite challenging. We
introduce FunLines, a competitive game where players edit news headlines to
make them funny, and where they rate the funniness of headlines edited by
others. FunLines makes the humor generation process fun, interactive,
collaborative, rewarding and educational, keeping players engaged and providing
humor data at a very low cost compared to traditional crowdsourcing approaches.
FunLines offers useful performance feedback, assisting players in getting
better over time at generating and assessing humor, as our analysis shows. This
helps to further increase the quality of the generated dataset. We show the
effectiveness of this data by training humor classification models that
outperform a previous benchmark, and we release this dataset to the public.
