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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)
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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)
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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)
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24
A Bayesian Framework for Information-Theoretic Probing
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021)
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25
How (Non-)Optimal is the Lexicon? ...
NAACL 2021 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021
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26
A Non-Linear Structural Probe ...
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27
How (Non-)Optimal is the Lexicon? ...
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28
Disambiguatory Signals are Stronger in Word-initial Positions ...
Pimentel, Tiago; Cotterell, Ryan; Roark, Brian. - : ETH Zurich, 2021
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29
Finding Concept-specific Biases in Form--Meaning Associations ...
NAACL 2021 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021
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30
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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31
Information-Theoretic Probing for Linguistic Structure ...
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32
Information-Theoretic Probing for Linguistic Structure ...
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33
A Corpus for Large-Scale Phonetic Typology ...
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34
Phonotactic Complexity and its Trade-offs ...
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35
Phonotactic Complexity and Its Trade-offs ...
Pimentel, Tiago; Roark, Brian; Cotterell, Ryan. - : ETH Zurich, 2020
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36
A Corpus for Large-Scale Phonetic Typology ...
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37
Predicting Declension Class from Form and Meaning ...
Abstract: The noun lexica of many natural languages are divided into several declension classes with characteristic morphological properties. Class membership is far from deterministic, but the phonological form of a noun and/or its meaning can often provide imperfect clues. Here, we investigate the strength of those clues. More specifically, we operationalize this by measuring how much information, in bits, we can glean about declension class from knowing the form and/or meaning of nouns. We know that form and meaning are often also indicative of grammatical gender---which, as we quantitatively verify, can itself share information with declension class---so we also control for gender. We find for two Indo-European languages (Czech and German) that form and meaning respectively share significant amounts of information with class (and contribute additional information above and beyond gender). The three-way interaction between class, form, and meaning (given gender) is also significant. Our study is important for two ... : 14 pages, 2 figures, the is the camera-ready version accepted at the 2020 Annual Conference of the Association for Computational Linguistics (ACL 2020) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2005.00626
https://dx.doi.org/10.48550/arxiv.2005.00626
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38
A corpus for large-scale phonetic typology
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39
Predicting declension class from form and meaning
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40
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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