  Contradictory results about the encoding of the semantic impact of negation
in pretrained language models (PLMs). have been drawn recently (e.g. Kassner
and Sch{\"u}tze (2020); Gubelmann and Handschuh (2022)). In this paper we focus
rather on the way PLMs encode negation and its formal impact, through the
phenomenon of the Negative Polarity Item (NPI) licensing in English. More
precisely, we use probes to identify which contextual representations best
encode 1) the presence of negation in a sentence, and 2) the polarity of a
neighboring masked polarity item. We find that contextual representations of
tokens inside the negation scope do allow for (i) a better prediction of the
presence of not compared to those outside the scope and (ii) a better
prediction of the right polarity of a masked polarity item licensed by not,
although the magnitude of the difference varies from PLM to PLM. Importantly,
in both cases the trend holds even when controlling for distance to not. This
tends to indicate that the embeddings of these models do reflect the notion of
negation scope, and do encode the impact of negation on NPI licensing. Yet,
further control experiments reveal that the presence of other lexical items is
also better captured when using the contextual representation of a token within
the same syntactic clause than outside from it, suggesting that PLMs simply
capture the more general notion of syntactic clause.
