# Using Sentence-Level LSTM Language Models for Script Inference

arXiv: https://arxiv.org/abs/1604.02993 · HTML: https://mlsanity.com/paper/1604.02993/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2016.

## Built on by (most cited first, 10 of 10)

- [Goal-Oriented Script Construction](https://mlsanity.com/paper/2107.13189/) (2021)
- [What do Large Language Models Learn about Scripts?](https://mlsanity.com/paper/2112.13834/) (2021)
- [Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding](https://mlsanity.com/paper/2203.05711/) (2022)
- [Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation](https://mlsanity.com/paper/2305.10907/) (2023)
- [Drafting Event Schemas using Language Models](https://mlsanity.com/paper/2305.14847/) (2023)
- [A Graph Enhanced BERT Model for Event Prediction](https://mlsanity.com/paper/2205.10822/) (2022)
- [Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering](https://mlsanity.com/paper/2108.10986/) (2021)
- [ScriptWorld: Text Based Environment For Learning Procedural Knowledge](https://mlsanity.com/paper/2307.03906/) (2023)
- [A Structured Clustering Approach for Inducing Media Narratives](https://mlsanity.com/paper/2604.10368/) (2026)
- [Scene Abstraction for Lexical Semantics: Structured Representations of Situated Meaning](https://mlsanity.com/paper/2605.22542/) (2026)

## Abstract (arXiv)

There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents. These systems operate on structured verb-argument events produced by an NLP pipeline. We compare these systems with recent Recurrent Neural Net models that directly operate on raw tokens to predict sentences, finding the latter to be roughly comparable to the former in terms of predicting missing events in documents.