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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 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 BASE Show details 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 BASE Show details 3 Don't Rule Out Monolingual Speakers: A Method For Crowdsourcing Machine Translation Data ... Bhatnagar, Rajat; Ganesh, Ananya; Kann, Katharina. - : arXiv, 2021 BASE Show details 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 BASE Show details 5 How to Adapt Your Pretrained Multilingual Model to 1600 Languages ... Ebrahimi, Abteen; Kann, Katharina. - : arXiv, 2021 Abstract: Pretrained multilingual models (PMMs) enable zero-shot learning via cross-lingual transfer, performing best for languages seen during pretraining. While methods exist to improve performance for unseen languages, they have almost exclusively been evaluated using amounts of raw text only available for a small fraction of the world's languages. In this paper, we evaluate the performance of existing methods to adapt PMMs to new languages using a resource available for over 1600 languages: the New Testament. This is challenging for two reasons: (1) the small corpus size, and (2) the narrow domain. While performance drops for all approaches, we surprisingly still see gains of up to $17.69\%$ accuracy for part-of-speech tagging and $6.29$ F1 for NER on average over all languages as compared to XLM-R. Another unexpected finding is that continued pretraining, the simplest approach, performs best. Finally, we perform a case study to disentangle the effects of domain and size and to shed light on the influence of the ... : Accepted to ACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2106.02124 https://dx.doi.org/10.48550/arxiv.2106.02124 BASE Hide details 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 BASE Show details 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 BASE Show details 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 Show details 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 Show details 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 Show details 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 BASE Show details 12 CLiMP: A Benchmark for Chinese Language Model Evaluation ... Xiang, Beilei; Yang, Changbing; Li, Yu. - : arXiv, 2021 BASE Show details 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) BASE Show details 14 Unsupervised Morphological Paradigm Completion ... Jin, Huiming; Cai, Liwei; Peng, Yihui. - : arXiv, 2020 BASE Show details 15 Learning to Learn Morphological Inflection for Resource-Poor Languages ... Kann, Katharina; Bowman, Samuel R.; Cho, Kyunghyun. - : arXiv, 2020 BASE Show details 16 Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge Kann, Katharina In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 17 Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings Yaghoobzadeh, Yadollah; Kann, Katharina; Hazen, Timothy. - : Ludwig-Maximilians-Universität München, 2019 BASE Show details 18 Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings ... Yaghoobzadeh, Yadollah; Kann, Katharina; Hazen, Timothy. - : Association for Computational Linguistics, 2019 BASE Show details 19 Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge ... Kann, Katharina. - : arXiv, 2019 BASE Show details 20 Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity ... Kann, Katharina. - : arXiv, 2019 BASE Show details Page: 1 2

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