Leshem Choshen

research

∙ 08/22/2023

Efficient Benchmarking (of Language Models)

The increasing versatility of language models LMs has given rise to a ne...

0 Yotam Perlitz, et al. ∙

research

∙ 06/02/2023

Resolving Interference When Merging Models

Transfer learning - i.e., further fine-tuning a pre-trained model on a d...

2 Prateek Yadav, et al. ∙

research

∙ 05/24/2023

MuLER: Detailed and Scalable Reference-based Evaluation

We propose a novel methodology (namely, MuLER) that transforms any refer...

0 Taelin Karidi, et al. ∙

research

∙ 03/16/2023

Jump to Conclusions: Short-Cutting Transformers With Linear Transformations

Transformer-based language models (LMs) create hidden representations of...

0 Alexander Yom Din, et al. ∙

research

∙ 02/09/2023

Knowledge is a Region in Weight Space for Fine-tuned Language Models

Research on neural networks has largely focused on understanding a singl...

0 Almog Gueta, et al. ∙

research

∙ 01/27/2023

Call for Papers – The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

We present the call for papers for the BabyLM Challenge: Sample-efficien...

0 Alex Warstadt, et al. ∙

research

∙ 12/02/2022

ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning

Pretraining has been shown to scale well with compute, data size and dat...

0 Shachar Don-Yehiya, et al. ∙

research

∙ 11/10/2022

DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering

Question answering models commonly have access to two sources of "knowle...

0 Ella Neeman, et al. ∙

research

∙ 10/31/2022

Where to start? Analyzing the potential value of intermediate models

Previous studies observed that finetuned models may be better base model...

0 Leshem Choshen, et al. ∙

research

∙ 10/06/2022

Reinforcement Learning with Large Action Spaces for Neural Machine Translation

Applying Reinforcement learning (RL) following maximum likelihood estima...

9 Asaf Yehudai, et al. ∙

research

∙ 08/02/2022

Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours

Text classification can be useful in many real-world scenarios, saving a...

0 Eyal Shnarch, et al. ∙

research

∙ 05/18/2022

PreQuEL: Quality Estimation of Machine Translation Outputs in Advance

We present the task of PreQuEL, Pre-(Quality-Estimation) Learning. A Pre...

0 Shachar Don-Yehiya, et al. ∙

research

∙ 05/11/2022

Some Grammatical Errors are Frequent, Others are Important

In Grammatical Error Correction, systems are evaluated by the number of ...

0 Leshem Choshen, et al. ∙

research

∙ 04/06/2022

Fusing finetuned models for better pretraining

Pretrained models are the standard starting point for training. This app...

0 Leshem Choshen, et al. ∙

research

∙ 03/20/2022

Cluster Tune: Boost Cold Start Performance in Text Classification

In real-world scenarios, a text classification task often begins with a ...

0 Eyal Shnarch, et al. ∙

research

∙ 10/13/2021

Semantics-aware Attention Improves Neural Machine Translation

The integration of syntactic structures into Transformer machine transla...

0 Aviv Slobodkin, et al. ∙

research

∙ 10/06/2021

On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation

To gain insight into the role neurons play, we study the activation patt...

0 Gal Patel, et al. ∙

research

∙ 09/13/2021

The Grammar-Learning Trajectories of Neural Language Models

The learning trajectories of linguistic phenomena provide insight into t...

1 Leshem Choshen, et al. ∙

research

∙ 08/23/2021

ComSum: Commit Messages Summarization and Meaning Preservation

We present ComSum, a data set of 7 million commit messages for text summ...

1 Leshem Choshen, et al. ∙

research

∙ 06/01/2021

Part of Speech and Universal Dependency effects on English Arabic Machine Translation

In this research paper, I will elaborate on a method to evaluate machine...

0 Ofek Rafaeli, et al. ∙

research

∙ 04/16/2021

Q^2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

Neural knowledge-grounded generative models for dialogue often produce c...

0 Or Honovich, et al. ∙

research

∙ 04/13/2021

Mediators in Determining what Processing BERT Performs First

Probing neural models for the ability to perform downstream tasks using ...

1 Aviv Slobodkin, et al. ∙

research

∙ 04/08/2021

GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns

Data exploration is an important step of every data science and machine ...

0 Piyawat Lertvittayakumjorn, et al. ∙

research

∙ 04/06/2021

SERRANT: a syntactic classifier for English Grammatical Error Types

SERRANT is a system and code for automatic classification of English gra...

1 Leshem Choshen, et al. ∙

research

∙ 01/29/2021

Transition based Graph Decoder for Neural Machine Translation

While a number of works showed gains from incorporating source-side symb...

1 Leshem Choshen, et al. ∙

research

∙ 10/21/2020

Classifying Syntactic Errors in Learner Language

We present a method for classifying syntactic errors in learner language...

1 Leshem Choshen, et al. ∙

research

∙ 10/19/2020

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

Approaching new data can be quite deterrent; you do not know how your ca...

0 Eyal Shnarch, et al. ∙

research

∙ 11/25/2019

Corpus Wide Argument Mining – a Working Solution

One of the main tasks in argument mining is the retrieval of argumentati...

0 Liat Ein-Dor, et al. ∙

research

∙ 09/15/2019

Automatically Extracting Challenge Sets for Non local Phenomena in Neural Machine Translation

We show that the state of the art Transformer Machine Translation(MT) mo...

0 Leshem Choshen, et al. ∙

research

∙ 09/15/2019

Automatically Extracting Challenge Sets for Non-local Phenomena Neural Machine Translation

We show that the state-of-the-art Transformer MT model is not biased tow...

0 Leshem Choshen, et al. ∙

research

∙ 07/21/2019

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

With the advancement in argument detection, we suggest to pay more atten...

0 Martin Gleize, et al. ∙

research

∙ 07/03/2019

On the Weaknesses of Reinforcement Learning for Neural Machine Translation

Reinforcement learning (RL) is frequently used to increase performance i...

0 Leshem Choshen, et al. ∙

research

∙ 06/10/2019

Learning to combine Grammatical Error Corrections

The field of Grammatical Error Correction (GEC) has produced various sys...

0 Yoav Kantor, et al. ∙

research

∙ 05/14/2019

The Language of Legal and Illegal Activity on the Darknet

The non-indexed parts of the Internet (the Darknet) have become a haven ...

0 Leshem Choshen, et al. ∙

research

∙ 03/06/2019

SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA

We present the SemEval 2019 shared task on UCCA parsing in English, Germ...

0 Daniel Hershcovich, et al. ∙

research

∙ 05/31/2018

SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation

We announce a shared task on UCCA parsing in English, German and French,...

0 Daniel Hershcovich, et al. ∙

research

∙ 04/30/2018

Inherent Biases in Reference-based Evaluation for Grammatical Error Correction and Text Simplification

The prevalent use of too few references for evaluating text-to-text gene...

0 Leshem Choshen, et al. ∙

research

∙ 04/30/2018

Automatic Metric Validation for Grammatical Error Correction

Metric validation in Grammatical Error Correction (GEC) is currently don...

0 Leshem Choshen, et al. ∙

research

∙ 04/11/2018

DORA The Explorer: Directed Outreaching Reinforcement Action-Selection

Exploration is a fundamental aspect of Reinforcement Learning, typically...

0 Leshem Choshen, et al. ∙

research

∙ 04/11/2018

Reference-less Measure of Faithfulness for Grammatical Error Correction

We propose USim, a semantic measure for Grammatical Error Correction (G...

0 Leshem Choshen, et al. ∙

Leshem Choshen

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