Facebook gamifies data collection to boost conversational AI

Facebook once piloted a text-based fantasy role-playing game to improve the conversational models powering things like its chatbots and smart speakers. In a preprint paper, researchers at the company describe a game that iterates between collecting data and retraining models on the collected data, with a metric that evaluates and compares models using players’ continuation rates (i.e., how long they keep playing). The coauthors claim that in experiments, they obtained data at a rate one-fifth the price per utterance of crowdsourcing and that their game provided evidence that lifelong dialogue learning is viable.

People learn to use language over the course of their lives from interactions they have with other people and the wider world, yet natural language processing (NLP) research often involves fixed data sets and frozen models. In this paradigm, models are prevented from interacting with humans at training time, a constraint that precludes performance improvements. An alternative is continually retraining the models, but this can be costly; many corpora are collected via crowdsourcing, where researchers pay crowdworkers through platforms like Amazon Mechanical Turk to perform tasks. Because the crowdworkers are motivated by pay rather than interest, budget overruns and poor-quality data can result.