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.
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.
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce.
In such low resources scenarios, we suggest performing an unsupervised classification task prior to fine-tuning on the target task.
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.
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.
In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog.
In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is.