Yonatan Belinkov

research

∙ 07/28/2022

Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions

Large amounts of training data are one of the major reasons for the high...

Yanai Elazar, et al. ∙

research

∙ 06/01/2022

IDANI: Inference-time Domain Adaptation via Neuron-level Interventions

Large pre-trained models are usually fine-tuned on downstream task data,...

Omer Antverg, et al. ∙

research

∙ 05/01/2022

MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

Huge language models (LMs) have ushered in a new era for AI, serving as ...

Ehud Karpas, et al. ∙

research

∙ 04/14/2022

How Gender Debiasing Affects Internal Model Representations, and Why It Matters

Common studies of gender bias in NLP focus either on extrinsic bias meas...

Hadas Orgad, et al. ∙

research

∙ 04/11/2022

A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference

Most evaluations of attribution methods focus on the English language. I...

Kerem Zaman, et al. ∙

research

∙ 02/10/2022

Locating and Editing Factual Knowledge in GPT

We investigate the mechanisms underlying factual knowledge recall in aut...

Kevin Meng, et al. ∙

research

∙ 10/14/2021

On the Pitfalls of Analyzing Individual Neurons in Language Models

While many studies have shown that linguistic information is encoded in ...

Omer Antverg, et al. ∙

research

∙ 09/09/2021

Debiasing Methods in Natural Language Understanding Make Bias More Accessible

Model robustness to bias is often determined by the generalization on ca...

Michael Mendelson, et al. ∙

research

∙ 08/31/2021

A Generative Approach for Mitigating Structural Biases in Natural Language Inference

Many natural language inference (NLI) datasets contain biases that allow...

Dimion Asael, et al. ∙

research

∙ 06/10/2021

Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models

Targeted syntactic evaluations have demonstrated the ability of language...

Matthew Finlayson, et al. ∙

research

∙ 06/10/2021

Variational Information Bottleneck for Effective Low-Resource Fine-Tuning

While large-scale pretrained language models have obtained impressive re...

Rabeeh Karimi Mahabadi, et al. ∙

research

∙ 04/16/2021

Natural Language Inference with a Human Touch: Using Human Explanations to Guide Model Attention

Natural Language Inference (NLI) models are known to learn from biases a...

Joe Stacey, et al. ∙

research

∙ 02/24/2021

Probing Classifiers: Promises, Shortcomings, and Alternatives

Probing classifiers have emerged as one of the prominent methodologies f...

Yonatan Belinkov, et al. ∙

research

∙ 12/02/2020

Learning from others' mistakes: Avoiding dataset biases without modeling them

State-of-the-art natural language processing (NLP) models often learn to...

Victor Sanh, et al. ∙

research

∙ 10/22/2020

Similarity Analysis of Self-Supervised Speech Representations

Self-supervised speech representation learning has recently been a prosp...

Yu-An Chung, et al. ∙

research

∙ 10/06/2020

Analyzing Individual Neurons in Pre-trained Language Models

While a lot of analysis has been carried to demonstrate linguistic knowl...

Nadir Durrani, et al. ∙

research

∙ 06/07/2020

Probing Neural Dialog Models for Conversational Understanding

The predominant approach to open-domain dialog generation relies on end-...

Abdelrhman Saleh, et al. ∙

research

∙ 05/04/2020

The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

Large-scale pretrained language models are the major driving force behin...

Mostafa Abdou, et al. ∙

research

∙ 05/03/2020

Similarity Analysis of Contextual Word Representation Models

This paper investigates contextual word representation models from the l...

John M. Wu, et al. ∙

research

∙ 04/26/2020

Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

Common methods for interpreting neural models in natural language proces...

Jesse Vig, et al. ∙

research

∙ 04/08/2020

Exploiting Redundancy in Pre-trained Language Models for Efficient Transfer Learning

Large pre-trained contextual word representations have transformed the f...

Fahim Dalvi, et al. ∙

research

∙ 11/08/2019

Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages

We introduce three memory-augmented Recurrent Neural Networks (MARNNs) a...

Mirac Suzgun, et al. ∙

research

∙ 11/01/2019

On the Linguistic Representational Power of Neural Machine Translation Models

Despite the recent success of deep neural networks in natural language p...

Yonatan Belinkov, et al. ∙

research

∙ 09/27/2019

A Constructive Prediction of the Generalization Error Across Scales

The dependency of the generalization error of neural networks on model a...

Jonathan S. Rosenfeld, et al. ∙

research

∙ 07/09/2019

On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference

Popular Natural Language Inference (NLI) datasets have been shown to be ...

Yonatan Belinkov, et al. ∙

research

∙ 07/09/2019

Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference

Natural Language Inference (NLI) datasets often contain hypothesis-only ...

Yonatan Belinkov, et al. ∙

research

∙ 07/09/2019

Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition

End-to-end neural network systems for automatic speech recognition (ASR)...

Yonatan Belinkov, et al. ∙

research

∙ 06/27/2019

Findings of the First Shared Task on Machine Translation Robustness

We share the findings of the first shared task on improving robustness o...

Xian Li, et al. ∙

research

∙ 06/20/2019

Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects

Visual question answering (VQA) models have been shown to over-rely on l...

Gabriel Grand, et al. ∙

research

∙ 06/09/2019

LSTM Networks Can Perform Dynamic Counting

In this paper, we systematically assess the ability of standard recurren...

Mirac Suzgun, et al. ∙

research

∙ 06/07/2019

Analyzing the Structure of Attention in a Transformer Language Model

The Transformer is a fully attention-based alternative to recurrent netw...

Jesse Vig, et al. ∙

research

∙ 06/04/2019

Improving Neural Language Models by Segmenting, Attending, and Predicting the Future

Common language models typically predict the next word given the context...

Hongyin Luo, et al. ∙

research

∙ 03/21/2019

Linguistic Knowledge and Transferability of Contextual Representations

Contextual word representations derived from large-scale neural language...

Nelson F. Liu, et al. ∙

research

∙ 02/02/2019

Character-based Surprisal as a Model of Human Reading in the Presence of Errors

Intuitively, human readers cope easily with errors in text; typos, missp...

Michael Hahn, et al. ∙

research

∙ 12/21/2018

NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks

We present a toolkit to facilitate the interpretation and understanding ...

Fahim Dalvi, et al. ∙

research

∙ 12/21/2018

What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models

Despite the remarkable evolution of deep neural networks in natural lang...

Fahim Dalvi, et al. ∙

research

∙ 12/21/2018

Analysis Methods in Neural Language Processing: A Survey

The field of natural language processing has seen impressive progress in...

Yonatan Belinkov, et al. ∙

research

∙ 11/03/2018

Identifying and Controlling Important Neurons in Neural Machine Translation

Neural machine translation (NMT) models learn representations containing...

Anthony Bau, et al. ∙

research

∙ 11/02/2018

On Evaluating the Generalization of LSTM Models in Formal Languages

Recurrent Neural Networks (RNNs) are theoretically Turing-complete and e...

Mirac Suzgun, et al. ∙

research

∙ 09/11/2018

Studying the History of the Arabic Language: Language Technology and a Large-Scale Historical Corpus

Arabic is a widely-spoken language with a long and rich history, but exi...

Yonatan Belinkov, et al. ∙

research

∙ 04/25/2018

On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference

We propose a process for investigating the extent to which sentence repr...

Adam Poliak, et al. ∙

research

∙ 01/23/2018

Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks

While neural machine translation (NMT) models provide improved translati...

Yonatan Belinkov, et al. ∙

research

∙ 11/06/2017

Synthetic and Natural Noise Both Break Neural Machine Translation

Character-based neural machine translation (NMT) models alleviate out-of...

Yonatan Belinkov, et al. ∙

research

∙ 09/13/2017

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

Neural models have become ubiquitous in automatic speech recognition sys...

Yonatan Belinkov, et al. ∙

research

∙ 09/02/2017

Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging

Word segmentation plays a pivotal role in improving any Arabic NLP appli...

Hassan Sajjad, et al. ∙

research

∙ 08/29/2017

Neural Machine Translation Training in a Multi-Domain Scenario

In this paper, we explore alternative ways to train a neural machine tra...

Hassan Sajjad, et al. ∙

research

∙ 04/11/2017

What do Neural Machine Translation Models Learn about Morphology?

Neural machine translation (MT) models obtain state-of-the-art performan...

Yonatan Belinkov, et al. ∙

research

∙ 12/28/2016

Shamela: A Large-Scale Historical Arabic Corpus

Arabic is a widely-spoken language with a rich and long history spanning...

Yonatan Belinkov, et al. ∙

research

∙ 08/15/2016

Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks

There is a lot of research interest in encoding variable length sentence...

Yossi Adi, et al. ∙

Yonatan Belinkov

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