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

Understand

Future predictions on sequence data (e.g., videos or audios) require the algorithms to capture non-Markovian and compositional properties of high-level semantics.

• Context-free grammars are natural choices to capture such properties, but traditional grammar parsers (e.g., Earley parser) only take symbolic sentences as inputs.
• In this paper, we generalize the Earley parser to parse sequence data which is neither segmented nor labeled.
• This generalized Earley parser integrates a grammar parser with a classifier to find the optimal segmentation and labels, and makes top-down future predictions.