21 |
A Non-Linear Structural Probe
|
|
|
|
In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
|
|
BASE
|
|
Show details
|
|
22 |
Disambiguatory Signals are Stronger in Word-initial Positions
|
|
|
|
In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
|
|
BASE
|
|
Show details
|
|
23 |
How (Non-)Optimal is the Lexicon?
|
|
|
|
In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
|
|
BASE
|
|
Show details
|
|
24 |
A Bayesian Framework for Information-Theoretic Probing
|
|
|
|
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
|
|
BASE
|
|
Show details
|
|
26 |
A Non-Linear Structural Probe ...
|
|
|
|
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 ...
|
|
URL: https://dx.doi.org/10.3929/ethz-b-000518983 http://hdl.handle.net/20.500.11850/518983
|
|
BASE
|
|
Hide details
|
|
28 |
Disambiguatory Signals are Stronger in Word-initial Positions ...
|
|
|
|
BASE
|
|
Show details
|
|
29 |
Finding Concept-specific Biases in Form--Meaning Associations ...
|
|
|
|
BASE
|
|
Show details
|
|
30 |
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
|
|
|
|
BASE
|
|
Show details
|
|
40 |
Pareto Probing: Trading Off Accuracy for Complexity
|
|
|
|
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
|
|
BASE
|
|
Show details
|
|
|
|