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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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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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Intrinsic Probing through Dimension Selection ...
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SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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The Paradigm Discovery Problem ...
Erdmann, Alexander; Elsner, Micha; Wu, Shijie. - : ETH Zurich, 2020
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Information-Theoretic Probing for Linguistic Structure ...
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It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information ...
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Information-Theoretic Probing for Linguistic Structure ...
Abstract: The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually "know" about natural language. Probes are a natural way of assessing this. When probing, a researcher chooses a linguistic task and trains a supervised model to predict annotations in that linguistic task from the network's learned representations. If the probe does well, the researcher may conclude that the representations encode knowledge related to the task. A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task itself. We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation. The ... : Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ...
URL: http://hdl.handle.net/20.500.11850/446005
https://dx.doi.org/10.3929/ethz-b-000446005
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Intrinsic Probing through Dimension Selection ...
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11
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing ...
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12
A Corpus for Large-Scale Phonetic Typology ...
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13
Phonotactic Complexity and its Trade-offs ...
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14
Phonotactic Complexity and Its Trade-offs ...
Pimentel, Tiago; Roark, Brian; Cotterell, Ryan. - : ETH Zurich, 2020
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15
A Corpus for Large-Scale Phonetic Typology ...
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16
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
Leung, Jun Yen; Emerson, Guy; Cotterell, Ryan. - : ETH Zurich, 2020
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17
Morphologically Aware Word-Level Translation ...
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18
Predicting Declension Class from Form and Meaning ...
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19
Predicting declension class from form and meaning
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20
Please Mind the Root: Decoding Arborescences for Dependency Parsing
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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