  Evaluating the veracity of everyday claims is time consuming and in some
cases requires domain expertise. We empirically demonstrate that the commonly
used fact checking pipeline, known as the retriever-reader, suffers from
performance deterioration when it is trained on the labeled data from one
domain and used in another domain. Afterwards, we delve into each component of
the pipeline and propose novel algorithms to address this problem. We propose
an adversarial algorithm to make the retriever component robust against
distribution shift. Our core idea is to initially train a bi-encoder on the
labeled source data, and then, to adversarially train two separate document and
claim encoders using unlabeled target data. We then focus on the reader
component and propose to train it such that it is insensitive towards the order
of claims and evidence documents. Our empirical evaluations support the
hypothesis that such a reader shows a higher robustness against distribution
shift. To our knowledge, there is no publicly available multi-topic fact
checking dataset. Thus, we propose a simple automatic method to re-purpose two
well-known fact checking datasets. We then construct eight fact checking
scenarios from these datasets, and compare our model to a set of strong
baseline models, including recent domain adaptation models that use GPT4 for
generating synthetic data.
