JerichoWorld

Dataset Information
Modalities
Texts
Languages
English
Introduced
2021
License
Unknown
Homepage

Overview

JerichoWorld is a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds.

JerichoWorld provides 24,198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7,836 heldout instances over 9 additional games in the test set.

Variants: JerichoWorld

Associated Benchmarks

This dataset is used in 2 benchmarks:

Recent Benchmark Submissions

Task Model Paper Date
Knowledge Graphs Worldformer Learning Knowledge Graph-based World Models … 2021-06-17
Knowledge Graphs GATA-W Learning Knowledge Graph-based World Models … 2021-06-17
Action Parsing Worldformer Learning Knowledge Graph-based World Models … 2021-06-17
Action Parsing CALM Learning Knowledge Graph-based World Models … 2021-06-17
Knowledge Graphs Rules Modeling Worlds in Text 2021-05-21
Knowledge Graphs Q*BERT Modeling Worlds in Text 2021-05-21
Action Parsing Seq2Seq Modeling Worlds in Text 2021-05-21
Knowledge Graphs Seq2Seq Modeling Worlds in Text 2021-05-21

Research Papers

Recent papers with results on this dataset: