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Future predictions on sequence data (e.g., videos or audios) require thealgorithms to capture non-Markovian and compositional properties of high-levelsemantics. Context-free grammars are natural choices to capture suchproperties, but traditional grammar parsers (e.g., Earley parser) only takesymbolic sentences as inputs. In this paper, we generalize the Earley parser toparse sequence data which is neither segmented nor labeled. This generalizedEarley parser integrates a grammar parser with a classifier to find the optimalsegmentation and labels, and makes top-down future predictions. Experimentsshow that our method significantly outperforms other approaches for futurehuman activity prediction.

Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction