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1
Morphological Processing of Low-Resource Languages: Where We Are and What's Next ...
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2
Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability ...
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3
Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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4
Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages ...
Abstract: We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization of this task was conducted as part of the fourth workshop on technologies for machine translation of low resource languages (LoResMT). Parallel corpora is presented and publicly available which includes the following directions: English$\leftrightarrow$Irish, English$\leftrightarrow$Marathi, and Taiwanese Sign language$\leftrightarrow$Traditional Chinese. Training data consists of 8112, 20933 and 128608 segments, respectively. There are additional monolingual data sets for Marathi and English that consist of 21901 segments. The results presented here are based on entries from a total of eight teams. Three teams submitted systems for English$\leftrightarrow$Irish while five teams submitted systems for English$\leftrightarrow$Marathi. Unfortunately, there were no systems submissions for the Taiwanese Sign ... : 10 pages ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2108.06598
https://dx.doi.org/10.48550/arxiv.2108.06598
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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 ...
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8
Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ...
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9
What Would a Teacher Do? {P}redicting Future Talk Moves ...
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10
How to Adapt Your Pretrained Multilingual Model to 1600 Languages ...
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11
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ...
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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 ...
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16
Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge
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. - : Ludwig-Maximilians-Universität München, 2019
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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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