Open domain entity state tracking aims to predict reasonable state changes of entities (i.e., [attribute] of [entity] was [before_state] and [after_state] afterwards) given the action descriptions. It's important to many reasoning tasks to support human everyday activities. However, it's challenging as the model needs to predict an arbitrary number of entity state changes caused by the action while most of the entities are implicitly relevant to the actions and their attributes as well as states are from open vocabularies. To tackle these challenges, we propose a novel end-to-end Knowledge Informed framework for open domain Entity State Tracking, namely KIEST, which explicitly retrieves the relevant entities and attributes from external knowledge graph (i.e., ConceptNet) and incorporates them to autoregressively generate all the entity state changes with a novel dynamic knowledge grained encoder-decoder framework. To enforce the logical coherence among the predicted entities, attributes, and states, we design a new constraint decoding strategy and employ a coherence reward to improve the decoding process. Experimental results show that our proposed KIEST framework significantly outperforms the strong baselines on the public benchmark dataset OpenPI.

翻译：开放域实体状态追踪旨在根据动作描述预测实体合理状态变化（即 [实体] 的 [属性] 由 [前状态] 变为 [后状态]）。该任务对支持人类日常活动的诸多推理任务至关重要。然而，其挑战性在于模型需预测由动作引发的任意数量的实体状态变化，且大多数实体与动作隐含相关，其属性及状态均来自开放词汇表。为应对这些挑战，我们提出一种新颖的端到端知识驱动开放域实体状态追踪框架——KIEST，该框架显式地从外部知识图谱（如 ConceptNet）中检索相关实体与属性，并利用新颖的动态知识粒度编码器-解码器框架将其整合，以自回归方式生成所有实体状态变化。为增强预测实体、属性及状态间的逻辑一致性，我们设计了一种新型约束解码策略，并采用一致性奖励优化解码过程。实验结果表明，所提出的 KIEST 框架在公开基准数据集 OpenPI 上显著优于多个强基线模型。