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Found 3 papers, 2 papers with code
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest.
Entity Set Co-Expansion in StackOverflow
Given a few seed entities of a certain type (e. g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds.
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set.
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