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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 26 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 BASE Show 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; Warstadt, Alex; Kann, Katharina. - : arXiv, 2021 Abstract: Linguistically informed analyses of language models (LMs) contribute to the understanding and improvement of these models. Here, we introduce the corpus of Chinese linguistic minimal pairs (CLiMP), which can be used to investigate what knowledge Chinese LMs acquire. CLiMP consists of sets of 1,000 minimal pairs (MPs) for 16 syntactic contrasts in Mandarin, covering 9 major Mandarin linguistic phenomena. The MPs are semi-automatically generated, and human agreement with the labels in CLiMP is 95.8%. We evaluated 11 different LMs on CLiMP, covering n-grams, LSTMs, and Chinese BERT. We find that classifier-noun agreement and verb complement selection are the phenomena that models generally perform best at. However, models struggle the most with the ba construction, binding, and filler-gap dependencies. Overall, Chinese BERT achieves an 81.8% average accuracy, while the performances of LSTMs and 5-grams are only moderately above chance level. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2101.11131 https://dx.doi.org/10.48550/arxiv.2101.11131 BASE Hide 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 Neural sequence-to-sequence models for low-resource morphology Kann, Katharina [Verfasser]; Schütze, Hinrich [Akademischer Betreuer]. - München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2019 DNB Subject Category Language Show details 18 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 19 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 20 Acquisition of Inflectional Morphology in Artificial Neural Networks With Prior Knowledge ... Kann, Katharina. - : arXiv, 2019 BASE Show details Page: 1 2

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