---
title: "Meta (Israel) — works"
description: "32 works by researchers of Meta (Israel) in our library, the most cited first."
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---
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# Works of Meta (Israel)

32 works · the most cited first · [← Meta (Israel)](https://md.research.elamur.ai/free/en/companies/meta-israel/)

1. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,752 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
2. [**Dense Passage Retrieval for Open-Domain Question Answering**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,370 citations*](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
3. [**wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations**Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, et al.*2020 · 1,355 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2020-wav2vec-2-0-framework-self/)
4. [**Supervised Learning of Universal Sentence Representations from Natural Language Inference Data**Alexis Conneau, Douwe Kiela, Holger Schwenk, et al.*2017 · 777 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2017-supervised-learning-universal-sentence-representations/)
5. [**SentEval: An Evaluation Toolkit for Universal Sentence Representations**Alexis Conneau, Douwe Kiela*2018 · 739 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/)
6. [**Neural Text Generation from Structured Data with Application to the Biography Domain**Remi Lebret, David Grangier, Michael Auli*2016 · 696 citations*](https://md.research.elamur.ai/free/en/papers/lebret-2016-neural-text-generation-structured-data/)
7. [**Globally Normalized Transition-Based Neural Networks**Daniel Andor, Chris Alberti, David Weiss, et al.*2016 · 676 citations*](https://md.research.elamur.ai/free/en/papers/andor-2016-globally-normalized-transition-based-neural/)
8. [**Mask-Predict: Parallel Decoding of Conditional Masked Language Models**Marjan Ghazvininejad, Omer Levy, Yinhan Liu, et al.*2019 · 671 citations*](https://md.research.elamur.ai/free/en/papers/ghazvininejad-2019-mask-predict-parallel-decoding-conditional/)
9. [**Unsupervised Machine Translation Using Monolingual Corpora Only**Guillaume Lample, Alexis Conneau, Ludovic Denoyer, et al.*2017 · 650 citations*](https://md.research.elamur.ai/free/en/papers/lample-2017-unsupervised-machine-translation-using-monolingual/)
10. [**Learning Word Vectors for 157 Languages**Edouard Grave, Piotr Bojanowski, Prakhar Gupta, et al.*2018 · 640 citations*](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/)
11. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 640 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
12. [**Advances in Pre-Training Distributed Word Representations**Tomas Mikolov, Edouard Grave, Piotr Bojanowski, et al.*2017 · 614 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/)
13. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
14. [**Pay Less Attention with Lightweight and Dynamic Convolutions**Felix Wu, Angela Fan, Alexei Baevski, et al.*2019 · 416 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-pay-less-attention-lightweight-dynamic/)
15. [**High Fidelity Neural Audio Compression**Alexandre Defossez, Jade Copet, Gabriel Synnaeve, et al.*2022 · 412 citations*](https://md.research.elamur.ai/free/en/papers/defossez-2022-high-fidelity-neural-audio-compression/)
16. [**Retrieval Augmentation Reduces Hallucination in Conversation**Kurt Shuster, Spencer Poff, Moya Chen, et al.*2021 · 382 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/)
17. [**Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection**Bertie Vidgen, Tristan Thrush, Zeerak Waseem, et al.*2021 · 320 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2021-learning-worst-dynamically-generated-datasets/)
18. [**A Neural Network Approach to Context-Sensitive Generation of Conversational Responses**Alessandro Sordoni, Michel Galley, Michael Auli, et al.*2015 · 307 citations*](https://md.research.elamur.ai/free/en/papers/sordoni-2015-neural-network-approach-context-sensitive/)
19. [**Learning Joint Multilingual Sentence Representations with Neural Machine Translation**Holger Schwenk, Matthijs Douze*2017 · 295 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2017-learning-joint-multilingual-sentence-representations/)
20. [**Information-Theoretic Probing for Linguistic Structure**Tiago Pimentel, Josef Valvoda, Rowan Hall Maudslay, et al.*2020 · 293 citations*](https://md.research.elamur.ai/free/en/papers/pimentel-2020-information-theoretic-probing-linguistic-structure/)
21. [**Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack**Emily Dinan, Samuel Humeau, Bharath Chintagunta, et al.*2019 · 291 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-build-it-break-it-fix/)
22. [**Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation**Emily Dinan, Angela Fan, Adina Williams, et al.*2020 · 265 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/)
23. [**Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq**Changhan Wang, Yun Tang, Xutai Ma, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-fairseq-s2t-fast-speech-text/)
24. [**Politeness Transfer: A Tag and Generate Approach**Aman Madaan, Amrith Setlur, Tanmay Parekh, et al.*2020 · 234 citations*](https://md.research.elamur.ai/free/en/papers/madaan-2020-politeness-transfer-tag-generate-approach/)
25. [**Classical Structured Prediction Losses for Sequence to Sequence Learning**Sergey Edunov, Myle Ott, Michael Auli, et al.*2018 · 232 citations*](https://md.research.elamur.ai/free/en/papers/edunov-2018-classical-structured-prediction-losses-sequence/)
26. [**Unsupervised Question Decomposition for Question Answering**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/)
27. [**LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders**Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, et al.*2024 · 209 citations*](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/)
28. [**Analyzing Uncertainty in Neural Machine Translation**Myle Ott, Michael Auli, David Grangier, et al.*2018 · 190 citations*](https://md.research.elamur.ai/free/en/papers/ott-2018-analyzing-uncertainty-neural-machine-translation/)
29. [**On The Evaluation of Machine Translation Systems Trained With Back-Translation**Sergey Edunov, Myle Ott, Marc'Aurelio Ranzato, et al.*2020 · 139 citations*](https://md.research.elamur.ai/free/en/papers/edunov-2020-evaluation-machine-translation-systems-trained/)
30. [**Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment**Philipp Koehn, Vishrav Chaudhary, Ahmed El-Kishky, et al.*2020 · 100 citations*](https://md.research.elamur.ai/free/en/papers/koehn-2020-findings-wmt-2020-shared-task/)
31. [**A Corpus for Multilingual Document Classification in Eight Languages**Holger Schwenk, Xian Li*2018 · 91 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2018-corpus-multilingual-document-classification-eight/)
32. [**A Survey of Qualitative Error Analysis for Neural Machine Translation Systems**Denise Díaz, James Cross, Vishrav Chaudhary, et al.*2020*](https://md.research.elamur.ai/free/en/papers/diaz-2020-survey-qualitative-error-analysis-neural/)
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