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.
Recent advances in large pretrained language models have increased attention to zero-shot text classification.
1 code implementation • 2 Aug 2022 • Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, Yoav Katz
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.
In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks.
We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components.
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.
no code implementations • 25 Nov 2019 • Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
Wikification of large corpora is beneficial for various NLP applications.