Constructing Temporal Dynamic Knowledge Graphs from Interactive
  Text-based Games
Keunwoo Peter Yu
In natural language processing, interactive text-based games serve as a test
bed for interactive AI systems. Prior work has proposed to play text-based
games by acting based on discrete knowledge graphs constructed by the Discrete
Graph Updater (DGU) to represent the game state from the natural language
description. While DGU has shown promising results with high interpretability,
it suffers from lower knowledge graph accuracy due to its lack of temporality
and limited generalizability to complex environments with objects with the same
label. In order to address DGU's weaknesses while preserving its high
interpretability, we propose the Temporal Discrete Graph Updater (TDGU), a
novel neural network model that represents dynamic knowledge graphs as a
sequence of timestamped graph events and models them using a temporal point
based graph neural network. Through experiments on the dataset collected from a
text-based game TextWorld, we show that TDGU outperforms the baseline DGU. We
further show the importance of temporal information for TDGU's performance
through an ablation study and demonstrate that TDGU has the ability to
generalize to more complex environments with objects with the same label. All
the relevant code can be found at
\url{https://github.com/yukw777/temporal-discrete-graph-updater}.