# Situated Language Learning via Interactive Narratives

arXiv: https://arxiv.org/abs/2103.09977 · HTML: https://mlsanity.com/paper/2103.09977/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 39 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 26 of 26)

- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [Interactive Fiction Games: A Colossal Adventure](https://mlsanity.com/paper/1909.05398/) (2019)
- [Learning to Speak and Act in a Fantasy Text Adventure Game](https://mlsanity.com/paper/1903.03094/) (2019)
- [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://mlsanity.com/paper/2001.08837/) (2020)
- [Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning](https://mlsanity.com/paper/1809.02121/) (2018)
- [Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning](https://mlsanity.com/paper/1812.01628/) (2018)
- [How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds](https://mlsanity.com/paper/2006.07409/) (2020)
- [Counting to Explore and Generalize in Text-based Games](https://mlsanity.com/paper/1806.11525/) (2018)
- [How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds](https://mlsanity.com/paper/2010.00685/) (2020)
- [Bringing Stories Alive: Generating Interactive Fiction Worlds](https://mlsanity.com/paper/2001.10161/) (2020)
- [Exploration Based Language Learning for Text-Based Games](https://mlsanity.com/paper/2001.08868/) (2020)
- [Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge](https://mlsanity.com/paper/2005.00811/) (2020)
- [Process-Level Representation of Scientific Protocols with Interactive Annotation](https://mlsanity.com/paper/2101.10244/) (2021)
- [A Roadmap towards Machine Intelligence](https://mlsanity.com/paper/1511.08130/) (2015)
- [Toward Automated Quest Generation in Text-Adventure Games](https://mlsanity.com/paper/1909.06283/) (2019)
- [Comprehensible Context-driven Text Game Playing](https://mlsanity.com/paper/1905.02265/) (2019)
- [Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Text](https://mlsanity.com/paper/1811.04319/) (2018)
- [A Paradigm for Situated and Goal-Driven Language Learning](https://mlsanity.com/paper/1610.03585/) (2016)

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

- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://mlsanity.com/paper/2211.08371/) (2022)
- [Situated Dialogue Learning through Procedural Environment Generation](https://mlsanity.com/paper/2110.03262/) (2021)

## Abstract (arXiv)

This paper provides a roadmap that explores the question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language in service of achieving a goal. We hypothesize that two key components in creating such agents are interactivity and environment grounding, shown to be vital parts of language learning in humans, and posit that interactive narratives should be the environments of choice for such training these agents. These games are simulations in which an agent interacts with the world through natural language -- "perceiving", "acting upon", and "talking to" the world using textual descriptions, commands, and dialogue -- and as such exist at the intersection of natural language processing, storytelling, and sequential decision making. We discuss the unique challenges a text games' puzzle-like structure combined with natural language state-and-action spaces provides: knowledge representation, commonsense reasoning, and exploration. Beyond the challenges described so far, progress in the realm of interactive narratives can be applied in adjacent problem domains. These applications provide interesting challenges of their own as well as extensions to those discussed so far. We describe three of them in detail: (1) evaluating AI system's commonsense understanding by automatically creating interactive narratives; (2) adapting abstract text-based policies to include other modalities such as vision; and (3) enabling multi-agent and human-AI collaboration in shared, situated worlds.