We explored various ways of systematically discovering and unifying latent and expressed expectations from nontraditional resources: • Language Universals: Language-independent rules and patterns; • Native Speaker: Interaction with native speakers through a machine-readable survey and supervised active learning; • Prior Mining: IL entity prior knowledge mining from both mono-lingual and cross-lingual corpora and knowledge bases; Furthermore, in emergent situations these expectations might not be available at once, and they may have different costs, so we need to organize and prioritize them to yield optimal performance within given time bounds.
we also experimented with various cost-aware composition methods with the input of acquired expectations, plus a time bound for development (1 hour, 2 hours), and the output as a wall-time schedule that determines the best sequence of applying modules and maximizes the use of all available resources.