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Found 13 papers, 5 papers with code
Active Learning for BERT: An Empirical Study
Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets.
Zero-Shot Text Classification with Self-Training
Recent advances in large pretrained language models have increased attention to zero-shot text classification.
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
Cluster & Tune: Boost Cold Start Performance in Text Classification
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce.
Cluster & Tune: Enhance BERT Performance in Low Resource Text Classification
In such low resources scenarios, we suggest performing an unsupervised classification task prior to fine-tuning on the target task.
Financial Event Extraction Using Wikipedia-Based Weak Supervision
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.
Corpus Wide Argument Mining -- a Working Solution
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
Fast End-to-End Wikification
Wikification of large corpora is beneficial for various NLP applications.
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion
When debating a controversial topic, it is often desirable to expand the boundaries of discussion.
Learning Sentiment Composition from Sentiment Lexicons
Sentiment composition is a fundamental sentiment analysis problem.
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks
We train a triplet network to embed sentences from the same section closer.
