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.
no code implementations • 12 Feb 2024 • Shir Ashury-Tahan, Benjamin Sznajder, Leshem Choshen, Liat Ein-Dor, Eyal Shnarch, Ariel Gera
DiffUse reduces the required amount of preference annotations, thus saving valuable time and resources in performing evaluation.
no code implementations • 25 Jan 2024 • Asaf Yehudai, Boaz Carmeli, Yosi Mass, Ofir Arviv, Nathaniel Mills, Assaf Toledo, Eyal Shnarch, Leshem Choshen
Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization.
no code implementations • 22 Aug 2023 • Yotam Perlitz, Elron Bandel, Ariel Gera, Ofir Arviv, Liat Ein-Dor, Eyal Shnarch, Noam Slonim, Michal Shmueli-Scheuer, Leshem Choshen
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities.
1 code implementation • 2 May 2023 • Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.
1 code implementation • 31 Oct 2022 • Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, Noam Slonim
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.
no code implementations • 29 Mar 2022 • Benjamin Sznajder, Chulaka Gunasekara, Guy Lev, Sachin Joshi, Eyal Shnarch, Noam Slonim
We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries.
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 • LREC 2022 • Piyawat Lertvittayakumjorn, Leshem Choshen, Eyal Shnarch, Francesca Toni
Data exploration is an important step of every data science and machine learning project, including those involving textual data.
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 • Findings of the Association for Computational Linguistics 2020 • Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Noam Slonim, Ranit Aharonov
Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory.
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 • 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 • 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 • EMNLP 2017 • Eyal Shnarch, Ran Levy, Vikas Raykar, Noam Slonim
A human observer may notice the following underlying common structure, or pattern: [someone][argue/suggest/state][that][topic term][sentiment term].