  Verb Sense Disambiguation is a well-known task in NLP, the aim is to find the
correct sense of a verb in a sentence. Recently, this problem has been extended
in a multimodal scenario, by exploiting both textual and visual features of
ambiguous verbs leading to a new problem, the Visual Verb Sense Disambiguation
(VVSD). Here, the sense of a verb is assigned considering the content of an
image paired with it rather than a sentence in which the verb appears.
Annotating a dataset for this task is more complex than textual disambiguation,
because assigning the correct sense to a pair of $<$image, verb$>$ requires
both non-trivial linguistic and visual skills. In this work, differently from
the literature, the VVSD task will be performed in a transductive
semi-supervised learning (SSL) setting, in which only a small amount of labeled
information is required, reducing tremendously the need for annotated data. The
disambiguation process is based on a graph-based label propagation method which
takes into account mono or multimodal representations for $<$image, verb$>$
pairs. Experiments have been carried out on the recently published dataset
VerSe, the only available dataset for this task. The achieved results
outperform the current state-of-the-art by a large margin while using only a
small fraction of labeled samples per sense. Code available:
https://github.com/GiBg1aN/TVVSD.
