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Hits 81 – 100 of 163

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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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83
SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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84
The Paradigm Discovery Problem ...
Erdmann, Alexander; Elsner, Micha; Wu, Shijie. - : ETH Zurich, 2020
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85
Information-Theoretic Probing for Linguistic Structure ...
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86
It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information ...
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87
Information-Theoretic Probing for Linguistic Structure ...
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88
Intrinsic Probing through Dimension Selection ...
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89
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing ...
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90
A Corpus for Large-Scale Phonetic Typology ...
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91
Phonotactic Complexity and its Trade-offs ...
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92
Phonotactic Complexity and Its Trade-offs ...
Pimentel, Tiago; Roark, Brian; Cotterell, Ryan. - : ETH Zurich, 2020
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93
A Corpus for Large-Scale Phonetic Typology ...
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94
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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95
Morphologically Aware Word-Level Translation ...
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96
Predicting Declension Class from Form and Meaning ...
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97
Predicting declension class from form and meaning
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98
Please Mind the Root: Decoding Arborescences for Dependency Parsing
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
Abstract: The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases.In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime.
URL: https://doi.org/10.3929/ethz-b-000462321
https://hdl.handle.net/20.500.11850/462321
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99
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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100
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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