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Found 4 papers, 3 papers with code
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models
Over the past few years, various domain-specific pretrained language models (PLMs) have been proposed and have outperformed general-domain PLMs in specialized areas such as biomedical, scientific, and clinical domains.
Ranked #1 on
Sentiment Analysis
on Financial PhraseBank
Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets
Although $K$-shot learning techniques can be applied, their performance tends to saturate when the number of annotations exceeds several tens of labels.
Data Augmentation for Neural Machine Translation using Generative Language Model
Data augmentation is a technique that enhances the performance of data-hungry models by generating synthetic data instead of collecting new ones.
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation
To take advantage of the high accuracy of human annotation and the cheap cost of distant supervision, we propose the dual supervision framework which effectively utilizes both types of data.
Ranked #45 on
Relation Extraction
on DocRED
