A Kumaran, Sujay Kumar Jauhar, and Sumit Basu
With the advent of the increasingly participatory Internet and the growing power of the crowd, “Serious Games” have proven to be a fertile approach for gathering task-specific natural language data at very low cost. In this paper we outline a game we call Doodling, based on the sketch-andconvey metaphor used in the popular board game Pictionary, with the goal of generating useful natural language data. We explore whether such a paradigm can be successfully extended for conveying more complex syntactic and semantic constructs than the words or short phrases typically used in the board game. Through a series of user experiments, we show that this is indeed the case, and that valuable parallel language data may be produced as a byproduct. In addition, we explore extensions to this paradigm along two axes – going online (vs. face-to-face) and going crosslingual. The results in each of the sets of experiments confirm the potential of Doodling game to generate data in large quantities and across languages, and thus provide a new means of developing data sets and technologies for resource-poor languages.
|Published in||proceedings of the Human Computation Workshop 2012|
|Publisher||American Association for Artificial Intelligence|
A Kumaran, Melissa Dunsmore, and Shaishav Kumar. Online Gaming for Crowdsourcing Phrase-equivalents, ACL – Association for Computational Linguistics, August 2014.