# Tracking Discrete and Continuous Entity State for Process Understanding

arXiv: https://arxiv.org/abs/1904.03518 · HTML: https://mlsanity.com/paper/1904.03518/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2019.

## Built on by (most cited first, 15 of 15)

- [Entity Tracking in Language Models](https://mlsanity.com/paper/2305.02363/) (2023)
- [Causal Reasoning of Entities and Events in Procedural Texts](https://mlsanity.com/paper/2301.10896/) (2023)
- [Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Events](https://mlsanity.com/paper/2104.07635/) (2021)
- [Coalescing Global and Local Information for Procedural Text Understanding](https://mlsanity.com/paper/2208.12848/) (2022)
- [OpenPI2.0: An Improved Dataset for Entity Tracking in Texts](https://mlsanity.com/paper/2305.14603/) (2023)
- [Language Modeling with Latent Situations](https://mlsanity.com/paper/2212.10012/) (2022)
- [LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training](https://mlsanity.com/paper/2201.08081/) (2022)
- [The Role of Semantic Parsing in Understanding Procedural Text](https://mlsanity.com/paper/2302.06829/) (2023)
- [50 Ways to Bake a Cookie: Mapping the Landscape of Procedural Texts](https://mlsanity.com/paper/2210.17235/) (2022)
- [Transferring Procedural Knowledge across Commonsense Tasks](https://mlsanity.com/paper/2304.13867/) (2023)
- [PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes](https://mlsanity.com/paper/2401.06930/) (2024)
- [Procedural Text Understanding via Scene-Wise Evolution](https://mlsanity.com/paper/2203.07600/) (2022)
- [Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding](https://mlsanity.com/paper/2210.06444/) (2022)
- [OpenPI-C: A Better Benchmark and Stronger Baseline for Open-Vocabulary State Tracking](https://mlsanity.com/paper/2306.00887/) (2023)
- [CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text](https://mlsanity.com/paper/2310.14326/) (2023)

## Abstract (arXiv)

Procedural text, which describes entities and their interactions as they undergo some process, depicts entities in a uniquely nuanced way. First, each entity may have some observable discrete attributes, such as its state or location; modeling these involves imposing global structure and enforcing consistency. Second, an entity may have properties which are not made explicit but can be effectively induced and tracked by neural networks. In this paper, we propose a structured neural architecture that reflects this dual nature of entity evolution. The model tracks each entity recurrently, updating its hidden continuous representation at each step to contain relevant state information. The global discrete state structure is explicitly modeled with a neural CRF over the changing hidden representation of the entity. This CRF can explicitly capture constraints on entity states over time, enforcing that, for example, an entity cannot move to a location after it is destroyed. We evaluate the performance of our proposed model on QA tasks over process paragraphs in the ProPara dataset and find that our model achieves state-of-the-art results.