  In this work we aim to understand and estimate the importance that a neural
network assigns to various aspects of the data while learning and making
predictions. Here we focus on the recognizing textual entailment (RTE) task and
its application to fact verification. In this context, the contributions of
this work are as follows. We investigate the attention weights a state of the
art RTE method assigns to input tokens in the RTE component of fact
verification systems, and confirm that most of the weight is assigned to POS
tags of nouns (e.g., NN, NNP etc.) or their phrases. To verify that these
lexicalized models transfer poorly, we implement a domain transfer experiment
where a RTE component is trained on the FEVER data, and tested on the Fake News
Challenge (FNC) dataset. As expected, even though this method achieves high
accuracy when evaluated in the same domain, the performance in the target
domain is poor, marginally above chance.To mitigate this dependence on
lexicalized information, we experiment with several strategies for masking out
names by replacing them with their semantic category, coupled with a unique
identifier to mark that the same or new entities are referenced between claim
and evidence. The results show that, while the performance on the FEVER dataset
remains at par with that of the model trained on lexicalized data, it improves
significantly when tested in the FNC dataset. Thus our experiments demonstrate
that our strategy is successful in mitigating the dependency on lexical
information.
