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1
Probing Classifiers: Promises, Shortcomings, and Advances ...
Belinkov, Yonatan. - : arXiv, 2021
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2
On the Pitfalls of Analyzing Individual Neurons in Language Models ...
Antverg, Omer; Belinkov, Yonatan. - : arXiv, 2021
Abstract: While many studies have shown that linguistic information is encoded in hidden word representations, few have studied individual neurons, to show how and in which neurons it is encoded. Among these, the common approach is to use an external probe to rank neurons according to their relevance to some linguistic attribute, and to evaluate the obtained ranking using the same probe that produced it. We show two pitfalls in this methodology: 1. It confounds distinct factors: probe quality and ranking quality. We separate them and draw conclusions on each. 2. It focuses on encoded information, rather than information that is used by the model. We show that these are not the same. We compare two recent ranking methods and a simple one we introduce, and evaluate them with regard to both of these aspects. ... : Accepted to ICLR 2022 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7
URL: https://dx.doi.org/10.48550/arxiv.2110.07483
https://arxiv.org/abs/2110.07483
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3
Debiasing Methods in Natural Language Understanding Make Bias More Accessible ...
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4
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ...
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5
Similarity Analysis of Contextual Word Representation Models ...
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6
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? ...
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7
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ...
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8
Analyzing Individual Neurons in Pre-trained Language Models ...
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9
On the Linguistic Representational Power of Neural Machine Translation Models
In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020)
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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
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11
Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
In: MIT Press (2019)
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12
On the Linguistic Representational Power of Neural Machine Translation Models ...
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13
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference ...
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14
Improving Neural Language Models by Segmenting, Attending, and Predicting the Future ...
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15
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
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16
On Evaluating the Generalization of LSTM Models in Formal Languages
Suzgun, Mirac; Belinkov, Yonatan; Shieber, Stuart. - : Society for Computation in Linguistics, 2019
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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
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18
LSTM Networks Can Perform Dynamic Counting
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19
On Evaluating the Generalization of LSTM Models in Formal Languages
In: Proceedings of the Society for Computation in Linguistics (2019)
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20
Analysis Methods in Neural Language Processing: A Survey
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 49-72 (2019) (2019)
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