  Modeling thematic fit (a verb--argument compositional semantics task)
currently requires a very large burden of labeled data. We take a
linguistically machine-annotated large corpus and replace corpus layers with
output from higher-quality, more modern taggers. We compare the old and new
corpus versions' impact on a verb--argument fit modeling task, using a
high-performing neural approach. We discover that higher annotation quality
dramatically reduces our data requirement while demonstrating better supervised
predicate-argument classification. But in applying the model to
psycholinguistic tasks outside the training objective, we see clear gains at
scale, but only in one of two thematic fit estimation tasks, and no clear gains
on the other. We also see that quality improves with training size, but perhaps
plateauing or even declining in one task. Last, we tested the effect of role
set size. All this suggests that the quality/quantity interplay is not all you
need. We replicate previous studies while modifying certain role representation
details and set a new state-of-the-art in event modeling, using a fraction of
the data. We make the new corpus version public.
