# Actionable Entities Recognition Benchmark for Interactive Fiction

arXiv: https://arxiv.org/abs/2109.13855 · HTML: https://mlsanity.com/paper/2109.13855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (2016)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [Keep CALM and Explore: Language Models for Action Generation in Text-based Games](https://mlsanity.com/paper/2010.02903/) (2020)
- [Adversarial Learning for Neural Dialogue Generation](https://mlsanity.com/paper/1701.06547/) (2017)
- [Language Understanding for Text-based Games Using Deep Reinforcement Learning](https://mlsanity.com/paper/1506.08941/) (2015)
- [Criticality in Formal Languages and Statistical Physics](https://mlsanity.com/paper/1606.06737/) (2016)
- [Story Realization: Expanding Plot Events into Sentences](https://mlsanity.com/paper/1909.03480/) (2019)
- [Movie Plot Analysis via Turning Point Identification](https://mlsanity.com/paper/1908.10328/) (2019)
- [Chinese Poetry Generation with Planning based Neural Network](https://mlsanity.com/paper/1610.09889/) (2016)
- [Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning](https://mlsanity.com/paper/1812.03509/) (2018)
- [MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants](https://mlsanity.com/paper/1905.09531/) (2019)
- [Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction](https://mlsanity.com/paper/1911.12511/) (2019)
- [What can you do with a rock? Affordance extraction via word embeddings](https://mlsanity.com/paper/1703.03429/) (2017)
- [Guess who? Multilingual approach for the automated generation of author-stylized poetry](https://mlsanity.com/paper/1807.07147/) (2018)
- [Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity](https://mlsanity.com/paper/2007.06290/) (2020)
- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)

## Built on by (most cited first, 2 of 2)

- [PLUGH: A Benchmark for Spatial Understanding and Reasoning in Large Language Models](https://mlsanity.com/paper/2408.04648/) (2024)
- [Branching Narratives: Character Decision Points Detection](https://mlsanity.com/paper/2405.07282/) (2024)

## Abstract (arXiv)

This paper presents a new natural language processing task - Actionable Entities Recognition (AER) - recognition of entities that protagonists could interact with for further plot development. Though similar to classical Named Entity Recognition (NER), it has profound differences. In particular, it is crucial for interactive fiction, where the agent needs to detect entities that might be useful in the future. We also discuss if AER might be further helpful for the systems dealing with narrative processing since actionable entities profoundly impact the causal relationship in a story. We validate the proposed task on two previously available datasets and present a new benchmark dataset for the AER task that includes 5550 descriptions with one or more actionable entities.