  Discovering latent topics from text corpora has been studied for decades.
Many existing topic models adopt a fully unsupervised setting, and their
discovered topics may not cater to users' particular interests due to their
inability of leveraging user guidance. Although there exist seed-guided topic
discovery approaches that leverage user-provided seeds to discover
topic-representative terms, they are less concerned with two factors: (1) the
existence of out-of-vocabulary seeds and (2) the power of pre-trained language
models (PLMs). In this paper, we generalize the task of seed-guided topic
discovery to allow out-of-vocabulary seeds. We propose a novel framework, named
SeeTopic, wherein the general knowledge of PLMs and the local semantics learned
from the input corpus can mutually benefit each other. Experiments on three
real datasets from different domains demonstrate the effectiveness of SeeTopic
in terms of topic coherence, accuracy, and diversity.
