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The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 (4)
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Hits 1 – 5 of 5
1
{PROST}: {P}hysical Reasoning about Objects through Space and Time ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Aroca-Ouellette, Stéphane
;
Kann, Katharina
. - : Underline Science Inc., 2021
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2
Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Bhatnagar, Rajat
;
Ganesh, Ananya
. - : Underline Science Inc., 2021
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3
What Would a Teacher Do? {P}redicting Future Talk Moves ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Ganesh, Ananya
;
Kann, Katharina
. - : Underline Science Inc., 2021
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4
How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Ebrahimi, Abteen
;
Kann, Katharina
. - : Underline Science Inc., 2021
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5
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
Kann, Katharina
In: Proceedings of the Society for Computation in Linguistics (2020)
Abstract:
How does knowledge of one language’s morphology influence learning of inflection rules in a second one? In order to investigate this question in artificial neural network models, we perform experiments with a sequence-to-sequence architecture, which we train on different combinations of eight source and three target languages. A detailed analysis of the model outputs suggests the following conclusions: (i) if source and target language are closely related, acquisition of the target language’s inflectional morphology constitutes an easier task for the model; (ii) knowledge of a prefixing (resp. suffixing) language makes acquisition of a suffixing (resp. prefixing) language’s morphology more challenging; and (iii) surprisingly, a source language which exhibits an agglutinative morphology simplifies learning of a second language’s inflectional morphology, independent of their relatedness.
Keyword:
Artificial Intelligence and Robotics
;
Computational Linguistics
;
Deep Learning
;
Inflection
;
Morphology
;
Neural Network
;
Pretraining
;
Transfer Learning
URL:
https://scholarworks.umass.edu/scil/vol3/iss1/11
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1112&context=scil
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