Yonatan Belinkov

Similarity Analysis of Self-Supervised Speech Representations

Self-supervised speech representation learning has recently been a prosp...
10/22/2020 ∙ by Yu-An Chung, et al. ∙ 0 ∙ share

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Analyzing Individual Neurons in Pre-trained Language Models

While a lot of analysis has been carried to demonstrate linguistic knowl...
10/06/2020 ∙ by Nadir Durrani, et al. ∙ 0 ∙ share

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Probing Neural Dialog Models for Conversational Understanding

The predominant approach to open-domain dialog generation relies on end-...
06/07/2020 ∙ by Abdelrhman Saleh, et al. ∙ 0 ∙ share

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The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

Large-scale pretrained language models are the major driving force behin...
05/04/2020 ∙ by Mostafa Abdou, et al. ∙ 0 ∙ share

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Similarity Analysis of Contextual Word Representation Models

This paper investigates contextual word representation models from the l...
05/03/2020 ∙ by John M. Wu, et al. ∙ 0 ∙ share

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

Common methods for interpreting neural models in natural language proces...
04/26/2020 ∙ by Jesse Vig, et al. ∙ 0 ∙ share

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

Large pre-trained contextual word representations have transformed the f...
04/08/2020 ∙ by Fahim Dalvi, et al. ∙ 0 ∙ share

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Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages

We introduce three memory-augmented Recurrent Neural Networks (MARNNs) a...
11/08/2019 ∙ by Mirac Suzgun, et al. ∙ 23 ∙ share

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On the Linguistic Representational Power of Neural Machine Translation Models

Despite the recent success of deep neural networks in natural language p...
11/01/2019 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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A Constructive Prediction of the Generalization Error Across Scales

The dependency of the generalization error of neural networks on model a...
09/27/2019 ∙ by Jonathan S. Rosenfeld, et al. ∙ 11 ∙ share

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

Popular Natural Language Inference (NLI) datasets have been shown to be ...
07/09/2019 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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

Natural Language Inference (NLI) datasets often contain hypothesis-only ...
07/09/2019 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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

End-to-end neural network systems for automatic speech recognition (ASR)...
07/09/2019 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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Findings of the First Shared Task on Machine Translation Robustness

We share the findings of the first shared task on improving robustness o...
06/27/2019 ∙ by Xian Li, et al. ∙ 0 ∙ share

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

Visual question answering (VQA) models have been shown to over-rely on l...
06/20/2019 ∙ by Gabriel Grand, et al. ∙ 1 ∙ share

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LSTM Networks Can Perform Dynamic Counting

In this paper, we systematically assess the ability of standard recurren...
06/09/2019 ∙ by Mirac Suzgun, et al. ∙ 0 ∙ share

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Analyzing the Structure of Attention in a Transformer Language Model

The Transformer is a fully attention-based alternative to recurrent netw...
06/07/2019 ∙ by Jesse Vig, et al. ∙ 0 ∙ share

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

Common language models typically predict the next word given the context...
06/04/2019 ∙ by Hongyin Luo, et al. ∙ 0 ∙ share

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Linguistic Knowledge and Transferability of Contextual Representations

Contextual word representations derived from large-scale neural language...
03/21/2019 ∙ by Nelson F. Liu, et al. ∙ 0 ∙ share

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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...
02/02/2019 ∙ by Michael Hahn, et al. ∙ 0 ∙ share

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NeuroX: A Toolkit for Analyzing Individual Neurons in Neural Networks

We present a toolkit to facilitate the interpretation and understanding ...
12/21/2018 ∙ by Fahim Dalvi, et al. ∙ 0 ∙ share

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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...
12/21/2018 ∙ by Fahim Dalvi, et al. ∙ 0 ∙ share

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Analysis Methods in Neural Language Processing: A Survey

The field of natural language processing has seen impressive progress in...
12/21/2018 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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Identifying and Controlling Important Neurons in Neural Machine Translation

Neural machine translation (NMT) models learn representations containing...
11/03/2018 ∙ by Anthony Bau, et al. ∙ 0 ∙ share

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On Evaluating the Generalization of LSTM Models in Formal Languages

Recurrent Neural Networks (RNNs) are theoretically Turing-complete and e...
11/02/2018 ∙ by Mirac Suzgun, et al. ∙ 0 ∙ share

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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...
09/11/2018 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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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...
04/25/2018 ∙ by Adam Poliak, et al. ∙ 0 ∙ share

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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...
01/23/2018 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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Synthetic and Natural Noise Both Break Neural Machine Translation

Character-based neural machine translation (NMT) models alleviate out-of...
11/06/2017 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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

Neural models have become ubiquitous in automatic speech recognition sys...
09/13/2017 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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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...
09/02/2017 ∙ by Hassan Sajjad, et al. ∙ 0 ∙ share

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Neural Machine Translation Training in a Multi-Domain Scenario

In this paper, we explore alternative ways to train a neural machine tra...
08/29/2017 ∙ by Hassan Sajjad, et al. ∙ 0 ∙ share

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What do Neural Machine Translation Models Learn about Morphology?

Neural machine translation (MT) models obtain state-of-the-art performan...
04/11/2017 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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Shamela: A Large-Scale Historical Arabic Corpus

Arabic is a widely-spoken language with a rich and long history spanning...
12/28/2016 ∙ by Yonatan Belinkov, et al. ∙ 0 ∙ share

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Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks

There is a lot of research interest in encoding variable length sentence...
08/15/2016 ∙ by Yossi Adi, et al. ∙ 0 ∙ share

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Yonatan Belinkov

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