# Automated Action Model Acquisition from Narrative Texts

arXiv: https://arxiv.org/abs/2307.10247 · HTML: https://mlsanity.com/paper/2307.10247/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [Narrative Planning: Balancing Plot and Character](https://mlsanity.com/paper/1401.3841/) (2014)
- [Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences](https://mlsanity.com/paper/1506.04089/) (2015)
- [GPT3-to-plan: Extracting plans from text using GPT-3](https://mlsanity.com/paper/2106.07131/) (2021)
- [The Causal News Corpus: Annotating Causal Relations in Event Sentences from News](https://mlsanity.com/paper/2204.11714/) (2022)
- [Extracting Action Sequences from Texts Based on Deep Reinforcement Learning](https://mlsanity.com/paper/1803.02632/) (2018)
- [Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach](https://mlsanity.com/paper/2101.10766/) (2021)
- [Towards Causality Extraction from Requirements](https://mlsanity.com/paper/2006.15871/) (2020)

## Built on by (most cited first, 3 of 3)

- [Large Language Models as Planning Domain Generators](https://mlsanity.com/paper/2405.06650/) (2024)
- [Branching Narratives: Character Decision Points Detection](https://mlsanity.com/paper/2405.07282/) (2024)
- [Structured Event Reasoning with Large Language Models](https://mlsanity.com/paper/2408.16098/) (2024)

## Abstract (arXiv)

Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narrative texts in an automated way is essential, but challenging because of the inherent complexities of such texts. We present NaRuto, a system that extracts structured events from narrative text and subsequently generates planning-language-style action models based on predictions of commonsense event relations, as well as textual contradictions and similarities, in an unsupervised manner. Experimental results in classical narrative planning domains show that NaRuto can generate action models of significantly better quality than existing fully automated methods, and even on par with those of semi-automated methods.