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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 21 – 34 of 34 21 Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ... 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; ., Nigel; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 22 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Nigel; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 23 Multilingual and Cross-Lingual Intent Detection from Spoken Data ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gerz, Daniela; Kusztos, Razvan. - : Underline Science Inc., 2021 BASE Show details 24 Semantic Data Set Construction from Human Clustering and Spatial Arrangement ... Majewska, Olga; McCarthy, Diana; Van Den Bosch, Jasper JF. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 25 AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ... Liu, Qianchu; Ponti, Edoardo M.; McCarthy, Diana. - : arXiv, 2021 BASE Show details 26 Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ... Liu, Fangyu; Vulić, Ivan; Korhonen, Anna. - : arXiv, 2021 BASE Show details 27 Parameter space factorization for zero-shot learning across tasks and languages Parovic, Marinela; Cotterell, Ryan; Vulić, Ivan... In: Transactions of the Association for Computational Linguistics, 9 (2021) BASE Show details 28 AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Korhonen, Anna; Liu, Qianchu. - : Underline Science Inc., 2021 BASE Show details 29 Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Nigel; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 30 LexFit: Lexical Fine-Tuning of Pretrained Language Models ... 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; Glavaš, Goran; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 31 Verb Knowledge Injection for Multilingual Event Processing ... 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; Glavaš, Goran; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 32 A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ... 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; ., Hinrich; Korhonen, Anna; Reichart, Roi; Shareghi, Ehsan; Vulić, Ivan; Zhao, Mengjie; Zhu, Yi. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.447 Abstract: Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25886-a-closer-look-at-few-shot-crosslingual-transfer-the-choice-of-shots-matters https://dx.doi.org/10.48448/m8s0-3a39 BASE Hide details 33 Is supervised syntactic parsing beneficial for language understanding tasks? An empirical investigation Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2021 BASE Show details 34 Evaluating multilingual text encoders for unsupervised cross-lingual retrieval Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer, 2021 BASE Show details Page: 1 2

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