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

arXiv: https://arxiv.org/abs/1806.03497 · HTML: https://mlsanity.com/paper/1806.03497/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [EgoTaskQA: Understanding Human Tasks in Egocentric Videos](https://mlsanity.com/paper/2210.03929/) (2022)
- [Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution](https://mlsanity.com/paper/2103.14230/) (2021)
- [ACRE: Abstract Causal REasoning Beyond Covariation](https://mlsanity.com/paper/2103.14232/) (2021)
- [Sequential Manipulation Planning on Scene Graph](https://mlsanity.com/paper/2207.04364/) (2022)
- [Learning Asynchronous and Sparse Human-Object Interaction in Videos](https://mlsanity.com/paper/2103.02758/) (2021)
- [Activity Grammars for Temporal Action Segmentation](https://mlsanity.com/paper/2312.04266/) (2023)
- [A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics](https://mlsanity.com/paper/2103.01403/) (2021)

## Abstract (arXiv)

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. Experiments show that our method significantly outperforms other approaches for future human activity prediction.