no code implementations • COLING 2014 • Noam Slonim, Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel, Mitesh Khapra, Tamar Lavee, Ran Levy, Paul Matchen, Anatoly Polnarov, Vikas Raykar, Ruty Rinott, Amrita Saha, Naama Zwerdling, David Konopnicki, Dan Gutfreund
no code implementations • 27 Sep 2016 • Orna Almogi, Lena Dankin, Nachum Dershowitz, Lior Wolf
We describe the course of a hackathon dedicated to the development of linguistic tools for Tibetan Buddhist studies.
no code implementations • ACL 2018 • Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim
We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks.
no code implementations • ACL 2019 • Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments.
no code implementations • WS 2019 • Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Shachar Mirkin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
To this end, we collected a large dataset of $400$ speeches in English discussing $200$ controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech.
no code implementations • IJCNLP 2019 • Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
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.
no code implementations • WS 2019 • Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
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.
no code implementations • 6 Oct 2020 • Kfir Bar, Nachum Dershowitz, Lena Dankin
We suggest a model for metaphor interpretation using word embeddings trained over a relatively large corpus.
no code implementations • 1 Jan 2021 • Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim
In such low resources scenarios, we suggest performing an unsupervised classification task prior to fine-tuning on the target task.
no code implementations • EMNLP (ArgMining) 2021 • Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, Noam Slonim
We describe the 2021 Key Point Analysis (KPA-2021) shared task on key point analysis that we organized as a part of the 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP 2021.
1 code implementation • 6 Jan 2022 • Liat Ein-Dor, Ilya Shnayderman, Artem Spector, Lena Dankin, Ranit Aharonov, Noam Slonim
In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks.
1 code implementation • ACL 2022 • Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce.
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.
1 code implementation • EMNLP 2020 • Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim
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.