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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 – 20 of 25
1
Morphological Processing of Low-Resource Languages: Where We Are and What's Next ...
Wiemerslage, Adam
;
Silfverberg, Miikka
;
Yang, Changbing
. - : arXiv, 2022
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2
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability ...
Fujinuma, Yoshinari
;
Boyd-Graber, Jordan
;
Kann, Katharina
. - : arXiv, 2022
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3
Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
Bhatnagar, Rajat
;
Ganesh, Ananya
;
Kann, Katharina
. - : arXiv, 2021
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4
Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages ...
Ojha, Atul Kr.
;
Liu, Chao-Hong
;
Kann, Katharina
. - : arXiv, 2021
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5
How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
Ebrahimi, Abteen
;
Kann, Katharina
. - : arXiv, 2021
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6
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas ...
Mager, Manuel
;
Oncevay, Arturo
;
Ebrahimi, Abteen
. - : Association for Computational Linguistics, 2021
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7
{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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8
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
BASE
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9
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
BASE
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10
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
BASE
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11
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ...
Ebrahimi, Abteen
;
Mager, Manuel
;
Oncevay, Arturo
. - : arXiv, 2021
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12
CLiMP: A Benchmark for Chinese Language Model Evaluation ...
Xiang, Beilei
;
Yang, Changbing
;
Li, Yu
. - : arXiv, 2021
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13
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
In: Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas. Edited by: Mager, Manuel; Oncevay, Arturo; Rios, Annette; Meza Ruiz, Ivan Vladimir; Palmer, Alexis; Neubig, Graham; Kann, Katharina (2021). Online: Association for Computational Linguistics. (2021)
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14
Unsupervised Morphological Paradigm Completion ...
Jin, Huiming
;
Cai, Liwei
;
Peng, Yihui
. - : arXiv, 2020
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15
Learning to Learn Morphological Inflection for Resource-Poor Languages ...
Kann, Katharina
;
Bowman, Samuel R.
;
Cho, Kyunghyun
. - : arXiv, 2020
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16
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
Kann, Katharina
In: Proceedings of the Society for Computation in Linguistics (2020)
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17
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
Yaghoobzadeh, Yadollah
;
Kann, Katharina
;
Hazen, Timothy
;
Agirre, Eneko
;
Schütze, Hinrich
. - : Ludwig-Maximilians-Universität München, 2019
Abstract:
Word embeddings typically represent differ- ent meanings of a word in a single conflated vector. Empirical analysis of embeddings of ambiguous words is currently limited by the small size of manually annotated resources and by the fact that word senses are treated as unrelated individual concepts. We present a large dataset based on manual Wikipedia an- notations and word senses, where word senses from different words are related by semantic classes. This is the basis for novel diagnos- tic tests for an embedding’s content: we probe word embeddings for semantic classes and an- alyze the embedding space by classifying em- beddings into semantic classes. Our main find- ings are: (i) Information about a sense is gen- erally represented well in a single-vector em- bedding – if the sense is frequent. (ii) A clas- sifier can accurately predict whether a word is single-sense or multi-sense, based only on its embedding. (iii) Although rare senses are not well represented in single-vector embed- dings, this does not have negative impact on an NLP application whose performance depends on frequent senses.
Keyword:
ddc:000
;
ddc:410
URL:
https://doi.org/10.5282/ubm/epub.72190
http://nbn-resolving.de/urn:nbn:de:bvb:19-epub-72190-4
https://epub.ub.uni-muenchen.de/72190/1/P19-1574.pdf
https://epub.ub.uni-muenchen.de/72190/
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18
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings ...
Yaghoobzadeh, Yadollah
;
Kann, Katharina
;
Hazen, Timothy
. - : Association for Computational Linguistics, 2019
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19
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge ...
Kann, Katharina
. - : arXiv, 2019
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20
Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity ...
Kann, Katharina
. - : arXiv, 2019
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