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Probing Classifiers: Promises, Shortcomings, and Advances ...
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On the Pitfalls of Analyzing Individual Neurons in Language Models ...
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Debiasing Methods in Natural Language Understanding Make Bias More Accessible ...
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Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ...
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Similarity Analysis of Contextual Word Representation Models ...
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Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? ...
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The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ...
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Analyzing Individual Neurons in Pre-trained Language Models ...
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Abstract:
While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid towards individual neurons.We carry outa neuron-level analysis using core linguistic tasks of predicting morphology, syntax and semantics, on pre-trained language models, with questions like: i) do individual neurons in pre-trained models capture linguistic information? ii) which parts of the network learn more about certain linguistic phenomena? iii) how distributed or focused is the information? and iv) how do various architectures differ in learning these properties? We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax. Our study also reveals interesting cross architectural comparisons. For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT ... : Accepted in EMNLP 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2010.02695 https://arxiv.org/abs/2010.02695
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On the Linguistic Representational Power of Neural Machine Translation Models
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In: Computational Linguistics, Vol 46, Iss 1, Pp 1-52 (2020) (2020)
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Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
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In: MIT Press (2019)
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On the Linguistic Representational Power of Neural Machine Translation Models ...
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On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference ...
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Improving Neural Language Models by Segmenting, Attending, and Predicting the Future ...
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On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
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On Evaluating the Generalization of LSTM Models in Formal Languages
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Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
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On Evaluating the Generalization of LSTM Models in Formal Languages
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Analysis Methods in Neural Language Processing: A Survey
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 49-72 (2019) (2019)
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