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A Non-Linear Structural Probe ...
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Abstract:
Probes are models devised to investigate the encoding of knowledge—e.g. syntactic structure—in contextual representations. Probes are often designed for simplicity, which has led to restrictions on probe design that may not allow for the full exploitation of the structure of encoded information; one such restriction is linearity. We examine the case of a structural probe (Hewitt and Manning, 2019), which aims to investigate the encoding of syntactic structure in contextual representations through learning only linear transformations. By observing that the structural probe learns a metric, we are able to kernelize it and develop a novel non-linear variant with an identical number of parameters. We test on 6 languages and find that the radial-basis function (RBF) kernel, in conjunction with regularization, achieves a statistically significant improvement over the baseline in all languages—implying that at least part of the syntactic knowledge is encoded non-linearly. We conclude by discussing how the RBF ... : Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ...
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URL: https://dx.doi.org/10.3929/ethz-b-000518983 http://hdl.handle.net/20.500.11850/518983
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23 |
Disambiguatory Signals are Stronger in Word-initial Positions ...
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24 |
Finding Concept-specific Biases in Form--Meaning Associations ...
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29 |
Pareto Probing: Trading Off Accuracy for Complexity
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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30 |
Speakers Fill Lexical Semantic Gaps with Context
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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31 |
Predicting Declension Class from Form and Meaning
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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32 |
A Tale of a Probe and a Parser
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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33 |
A Corpus for Large-Scale Phonetic Typology
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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34 |
Phonotactic Complexity and Its Trade-offs
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In: Transactions of the Association for Computational Linguistics, 8 (2020)
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35 |
Information-Theoretic Probing for Linguistic Structure
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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36 |
Metaphor Detection Using Context and Concreteness
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In: Proceedings of the Second Workshop on Figurative Language Processing (2020)
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39 |
Rethinking Phonotactic Complexity
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In: Proceedings of the Society for Computation in Linguistics (2019)
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