Search Results for author: Eyal Shnarch

Found 15 papers, 5 papers with code

Active Learning for BERT: An Empirical Study

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.

Active Learning Binary text classification +4

Zero-Shot Text Classification with Self-Training

1 code implementation31 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.

Classification Natural Language Inference +4

Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization

no code implementations29 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.

Decision Making

Cluster & Tune: Enhance BERT Performance in Low Resource Text Classification

no code implementations1 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.

Classification General Classification +2

Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains

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.

Domain Adaptation

Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network

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.

GRASP: Rich Patterns for Argumentation Mining

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].

Document Classification

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