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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 27 1 Probing Classifiers: Promises, Shortcomings, and Advances ... Belinkov, Yonatan. - : arXiv, 2021 BASE Show details 2 On the Pitfalls of Analyzing Individual Neurons in Language Models ... Antverg, Omer; Belinkov, Yonatan. - : arXiv, 2021 BASE Show details 3 Debiasing Methods in Natural Language Understanding Make Bias More Accessible ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Belinkov, Yonatan; Mendelson, Michael. - : Underline Science Inc., 2021 BASE Show details 4 Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Belinkov, Yonatan; Finlayson, Matthew. - : Underline Science Inc., 2021 BASE Show details 5 Similarity Analysis of Contextual Word Representation Models ... Wu, John M.; Belinkov, Yonatan; Sajjad, Hassan. - : arXiv, 2020 BASE Show details 6 Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? ... Ravichander, Abhilasha; Belinkov, Yonatan; Hovy, Eduard. - : arXiv, 2020 Abstract: Although neural models have achieved impressive results on several NLP benchmarks, little is understood about the mechanisms they use to perform language tasks. Thus, much recent attention has been devoted to analyzing the sentence representations learned by neural encoders, through the lens of `probing' tasks. However, to what extent was the information encoded in sentence representations, as discovered through a probe, actually used by the model to perform its task? In this work, we examine this probing paradigm through a case study in Natural Language Inference, showing that models can learn to encode linguistic properties even if they are not needed for the task on which the model was trained. We further identify that pretrained word embeddings play a considerable role in encoding these properties rather than the training task itself, highlighting the importance of careful controls when designing probing experiments. Finally, through a set of controlled synthetic tasks, we demonstrate models can encode ... : EACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2005.00719 https://arxiv.org/abs/2005.00719 BASE Hide details 7 The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ... Abdou, Mostafa; Ravishankar, Vinit; Barrett, Maria. - : arXiv, 2020 BASE Show details 8 Analyzing Individual Neurons in Pre-trained Language Models ... Durrani, Nadir; Sajjad, Hassan; Dalvi, Fahim. - : arXiv, 2020 BASE Show details 9 On the Linguistic Representational Power of Neural Machine Translation Models Belinkov, Yonatan; Durrani, Nadir; Dalvi, Fahim... In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020) BASE Show details 10 Studying the history of the Arabic language: language technology and a large-scale historical corpus [<Journal>] Shmidman, Avi [Verfasser]; Romanov, Maxim [Verfasser]; Barrón-Cedeño, Alberto [Verfasser]. DNB Subject Category Language Show details 11 Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment Barzilay, Regina; Lei, Tao; Globerson, Amir... In: MIT Press (2019) BASE Show details 12 On the Linguistic Representational Power of Neural Machine Translation Models ... Belinkov, Yonatan; Durrani, Nadir; Dalvi, Fahim. - : arXiv, 2019 BASE Show details 13 On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference ... Belinkov, Yonatan; Poliak, Adam; Shieber, Stuart M.. - : arXiv, 2019 BASE Show details 14 Improving Neural Language Models by Segmenting, Attending, and Predicting the Future ... Luo, Hongyin; Jiang, Lan; Belinkov, Yonatan. - : arXiv, 2019 BASE Show details 15 On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference Belinkov, Yonatan; Poliak, Adam; Shieber, Stuart. - 2019 BASE Show details 16 On Evaluating the Generalization of LSTM Models in Formal Languages Suzgun, Mirac; Belinkov, Yonatan; Shieber, Stuart. - : Society for Computation in Linguistics, 2019 BASE Show details 17 Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference Belinkov, Yonatan; Poliak, Adam; Shieber, Stuart. - : Association of Computational Linguistics, 2019 BASE Show details 18 LSTM Networks Can Perform Dynamic Counting Suzgun, Mirac; Gehrmann, Sebastian; Belinkov, Yonatan. - 2019 BASE Show details 19 On Evaluating the Generalization of LSTM Models in Formal Languages Suzgun, Mirac; Belinkov, Yonatan; Shieber, Stuart M. In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 20 Analysis Methods in Neural Language Processing: A Survey Belinkov, Yonatan; Glass, James In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 49-72 (2019) (2019) BASE Show details Page: 1 2

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