Search Results for author: Yanai Elazar

Found 24 papers, 12 papers with code

It’s not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT

1 code implementation EMNLP (BlackboxNLP) 2020 Hila Gonen, Shauli Ravfogel, Yanai Elazar, Yoav Goldberg

Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.

Translation

Text-based NP Enrichment

1 code implementation24 Sep 2021 Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty

Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding.

Natural Language Understanding

Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema

no code implementations EMNLP 2021 Yanai Elazar, Hongming Zhang, Yoav Goldberg, Dan Roth

To support this claim, we first show that the current evaluation method of WS is sub-optimal and propose a modification that uses twin sentences for evaluation.

Bias Detection Disentanglement +1

Contrastive Explanations for Model Interpretability

1 code implementation EMNLP 2021 Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, Yoav Goldberg

Our method is based on projecting model representation to a latent space that captures only the features that are useful (to the model) to differentiate two potential decisions.

Text Classification

First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT

1 code implementation EACL 2021 Benjamin Muller, Yanai Elazar, Benoît Sagot, Djamé Seddah

Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning.

Language Modelling Pretrained Language Models +1

It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT

1 code implementation16 Oct 2020 Hila Gonen, Shauli Ravfogel, Yanai Elazar, Yoav Goldberg

Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.

Translation

Evaluating NLP Models via Contrast Sets

no code implementations1 Oct 2020 Matt Gardner, Yoav Artzi, Victoria Basmova, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hanna Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, A. Zhang, Ben Zhou

Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.

Reading Comprehension Sentiment Analysis

Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals

no code implementations1 Jun 2020 Yanai Elazar, Shauli Ravfogel, Alon Jacovi, Yoav Goldberg

In this work, we point out the inability to infer behavioral conclusions from probing results and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded.

Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection

1 code implementation ACL 2020 Shauli Ravfogel, Yanai Elazar, Hila Gonen, Michael Twiton, Yoav Goldberg

The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models.

Fairness Multi-class Classification +1

oLMpics -- On what Language Model Pre-training Captures

1 code implementation31 Dec 2019 Alon Talmor, Yanai Elazar, Yoav Goldberg, Jonathan Berant

A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data.

Language Modelling

Adversarial Removal of Demographic Attributes Revisited

no code implementations IJCNLP 2019 Maria Barrett, Yova Kementchedjhieva, Yanai Elazar, Desmond Elliott, Anders S{\o}gaard

Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on.

At Your Fingertips: Automatic Piano Fingering Detection

no code implementations25 Sep 2019 Amit Moryossef, Yanai Elazar, Yoav Goldberg

Automatic Piano Fingering is a hard task which computers can learn using data.

Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution

1 code implementation26 May 2019 Yanai Elazar, Yoav Goldberg

We provide the first computational treatment of fused-heads constructions (FH), focusing on the numeric fused-heads (NFH).

Missing Elements

Where's My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution

no code implementations TACL 2019 Yanai Elazar, Yoav Goldberg

We provide the first computational treatment of fused-heads constructions (FHs), focusing on the numeric fused-heads (NFHs).

Adversarial Removal of Demographic Attributes from Text Data

1 code implementation EMNLP 2018 Yanai Elazar, Yoav Goldberg

Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation.

Representation Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.