Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds

Anonymous

Paper Link: https://openreview.net/forum?id=MQMzj4jKdac

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: 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.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Yu Zhang

Copyright Consent Name And Address: University of Illinois at Urbana-Champaign, 201 N. Goodwin Ave, Urbana, IL 61801