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}.

翻译：在自然语言处理中，交互式文本游戏为交互式人工智能系统提供了测试平台。先前研究提出通过离散图更新器（DGU）构建基于自然语言描述的游戏状态表示离散知识图谱，并据此执行文本游戏操作。尽管DGU凭借高可解释性取得了良好效果，但由于缺乏时序性且难以泛化至包含同标签对象的复杂环境，其知识图谱的准确性较低。为弥补DGU的不足同时保持其高可解释性，我们提出时序离散图更新器（TDGU）——一种新型神经网络模型。该模型将动态知识图谱表示为带时间戳的图事件序列，并通过基于时序点的图神经网络进行建模。在从文本游戏TextWorld收集的数据集上进行的实验表明，TDGU优于基线DGU。通过消融研究进一步证明时序信息对TDGU性能的重要性，并验证了TDGU能够泛化至包含同标签对象的更复杂环境。相关代码详见\url{https://github.com/yukw777/temporal-discrete-graph-updater}。