  In parallel to their overwhelming success across NLP tasks, language ability
of deep Transformer networks, pretrained via language modeling (LM) objectives
has undergone extensive scrutiny. While probing revealed that these models
encode a range of syntactic and semantic properties of a language, they are
still prone to fall back on superficial cues and simple heuristics to solve
downstream tasks, rather than leverage deeper linguistic knowledge. In this
paper, we target one such area of their deficiency, verbal reasoning. We
investigate whether injecting explicit information on verbs' semantic-syntactic
behaviour improves the performance of LM-pretrained Transformers in event
extraction tasks -- downstream tasks for which accurate verb processing is
paramount. Concretely, we impart the verb knowledge from curated lexical
resources into dedicated adapter modules (dubbed verb adapters), allowing it to
complement, in downstream tasks, the language knowledge obtained during
LM-pretraining. We first demonstrate that injecting verb knowledge leads to
performance gains in English event extraction. We then explore the utility of
verb adapters for event extraction in other languages: we investigate (1)
zero-shot language transfer with multilingual Transformers as well as (2)
transfer via (noisy automatic) translation of English verb-based lexical
constraints. Our results show that the benefits of verb knowledge injection
indeed extend to other languages, even when verb adapters are trained on
noisily translated constraints.
