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1
A Latent-Variable Model for Intrinsic Probing ...
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Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ...
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3
ANLIzing the Adversarial Natural Language Inference Dataset
In: Proceedings of the Society for Computation in Linguistics (2022)
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4
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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6
Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
In: Transactions of the Association for Computational Linguistics, 9 (2021)
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8
UnNatural Language Inference ...
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9
Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.230/ Abstract: A possible explanation for the impressive performance of masked language model (MLM) pre-training is that such models have learned to represent the syntactic structures prevalent in classical NLP pipelines. In this paper, we propose a different explanation: MLMs succeed on downstream tasks almost entirely due to their ability to model higher-order word co-occurrence statistics. To demonstrate this, we pre-train MLMs on sentences with randomly shuffled word order, and show that these models still achieve high accuracy after fine-tuning on many downstream tasks -- including on tasks specifically designed to be challenging for models that ignore word order. Our models perform surprisingly well according to some parametric syntactic probes, indicating possible deficiencies in how we test representations for syntactic information. Overall, our results show that purely distributional information largely explains the success of ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://dx.doi.org/10.48448/3r0a-fw32
https://underline.io/lecture/37423-masked-language-modeling-and-the-distributional-hypothesis-order-word-matters-pre-training-for-little
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10
Information-Theoretic Probing for Linguistic Structure ...
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11
Intrinsic Probing through Dimension Selection ...
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12
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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13
Pareto Probing: Trading Off Accuracy for Complexity
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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14
Predicting Declension Class from Form and Meaning
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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15
A Tale of a Probe and a Parser
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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16
Intrinsic Probing through Dimension Selection
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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17
Information-Theoretic Probing for Linguistic Structure
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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18
A Tale of a Probe and a Parser ...
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19
Measuring the Similarity of Grammatical Gender Systems by Comparing Partitions ...
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20
Predicting Declension Class from Form and Meaning ...
Williams, Adina; Pimentel, Tiago; Blix, Hagen. - : ETH Zurich, 2020
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