Search Results for author: Liat Ein-Dor

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 +3

Label-Efficient Model Selection for Text Generation

no code implementations12 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.

Model Selection Text Generation

Efficient Benchmarking of Language Models

no code implementations22 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.


Active Learning for Natural Language Generation

no code implementations24 May 2023 Yotam Perlitz, Ariel Gera, Michal Shmueli-Scheuer, Dafna Sheinwald, Noam Slonim, Liat Ein-Dor

In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model.

Active Learning text-classification +2

SimpleStyle: An Adaptable Style Transfer Approach

no code implementations20 Dec 2022 Elron Bandel, Yoav Katz, Noam Slonim, Liat Ein-Dor

We offer our protocol as a simple yet strong baseline for works that wish to make incremental advancements in the field of attribute controlled text rewriting.

Attribute Denoising +2

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.

Natural Language Inference text-classification +2

Diversity Enhanced Table-to-Text Generation via Type Control

no code implementations22 May 2022 Yotam Perlitz, Liat Ein-Dor, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer

Generating natural language statements to convey logical inferences from tabular data (i. e., Logical NLG) is a process with one input and a variety of valid outputs.

Table-to-Text Generation valid +1

Quality Controlled Paraphrase Generation

1 code implementation ACL 2022 Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor

Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.

Paraphrase Generation Sentence

Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis

1 code implementation6 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.

Continual Pretraining Sentiment Analysis

Advances in Debating Technologies: Building AI That Can Debate Humans

no code implementations ACL 2021 Roy Bar-Haim, Liat Ein-Dor, Matan Orbach, Elad Venezian, Noam Slonim

We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components.

Argument Mining Stance Classification

Financial Event Extraction Using Wikipedia-Based Weak Supervision

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.

BIG-bench Machine Learning Event Extraction

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