  Task-oriented dialog (TOD) agents often ground their responses on external
knowledge bases (KBs). These KBs can be dynamic and may be updated frequently.
Existing approaches for learning TOD agents assume the KB snapshot contemporary
to each individual dialog is available during training. However, in real-world
scenarios, only the latest KB snapshot is available during training and as a
result, the train dialogs may contain facts conflicting with the latest KB.
These dialog-KB inconsistencies in the training data may potentially confuse
the TOD agent learning algorithm.
  In this work, we define the novel problem of learning a TOD agent with
dialog-KB inconsistencies in the training data. We propose a Dialog-KB
Arbitration Framework (DKAF) which reduces the dialog-KB inconsistencies by
predicting the contemporary KB snapshot for each train dialog. These predicted
KB snapshots are then used for training downstream TOD agents. As there are no
existing datasets with dialog-KB inconsistencies, we systematically introduce
inconsistencies in two publicly available dialog datasets. We show that TOD
agents trained with DKAF perform better than existing baselines on both these
datasets
