  This paper discusses the current critique against neural network-based
Natural Language Understanding (NLU) solutions known as language models. We
argue that much of the current debate rests on an argumentation error that we
will refer to as the singleton fallacy: the assumption that language, meaning,
and understanding are single and uniform phenomena that are unobtainable by
(current) language models. By contrast, we will argue that there are many
different types of language use, meaning and understanding, and that (current)
language models are build with the explicit purpose of acquiring and
representing one type of structural understanding of language. We will argue
that such structural understanding may cover several different modalities, and
as such can handle several different types of meaning. Our position is that we
currently see no theoretical reason why such structural knowledge would be
insufficient to count as "real" understanding.
