  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.
