no code implementations • 8 Aug 2024 • Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, Noam Slonim
We propose Conversational Prompt Engineering (CPE), a user-friendly tool that helps users create personalized prompts for their specific tasks.
no code implementations • 25 Jul 2024 • Alon Halfon, Shai Gretz, Ofir Arviv, Artem Spector, Orith Toledo-Ronen, Yoav Katz, Liat Ein-Dor, Michal Shmueli-Scheuer, Noam Slonim
Here, we provide recommended HP configurations for practical use-cases that represent a better starting point for practitioners, when considering two SOTA LLMs and two commonly used tuning methods.
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.
2 code implementations • EMNLP 2021 • Matan Orbach, Orith Toledo-Ronen, Artem Spector, Ranit Aharonov, Yoav Katz, Noam Slonim
Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets.
Ranked #1 on
Aspect Extraction
on YASO - YELP
no code implementations • Findings of the Association for Computational Linguistics 2020 • Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu, Artem Spector, Noam Slonim
The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets.
no code implementations • 19 Aug 2019 • Ilya Shnayderman, Liat Ein-Dor, Yosi Mass, Alon Halfon, Benjamin Sznajder, Artem Spector, Yoav Katz, Dafna Sheinwald, Ranit Aharonov, Noam Slonim
Wikification of large corpora is beneficial for various NLP applications.
no code implementations • EMNLP 2018 • Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data.