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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 Hits 1 – 10 of 10 1 IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ... Bugliarello, Emanuele; Liu, Fangyu; Pfeiffer, Jonas. - : arXiv, 2022 BASE Show details 2 Improving Word Translation via Two-Stage Contrastive Learning ... Li, Yaoyiran; Liu, Fangyu; Collier, Nigel. - : arXiv, 2022 BASE Show details 3 Prix-LM: Pretraining for Multilingual Knowledge Base Construction ... Zhou, Wenxuan; Liu, Fangyu; Vulić, Ivan. - : arXiv, 2021 BASE Show details 4 Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ... Liu, Fangyu; Vulić, Ivan; Korhonen, Anna. - : arXiv, 2021 BASE Show details 5 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... Liu, Qianchu; Liu, Fangyu; Collier, Nigel; Korhonen, Anna; Vulić, Ivan. - : arXiv, 2021 Abstract: Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques. Inspired by this line of work, in this paper we propose a fully unsupervised approach to improving word-in-context (WiC) representations in PLMs, achieved via a simple and efficient WiC-targeted fine-tuning procedure: MirrorWiC. The proposed method leverages only raw texts sampled from Wikipedia, assuming no sense-annotated data, and learns context-aware word representations within a standard contrastive learning setup. We experiment with a series of standard and comprehensive WiC benchmarks across multiple languages. Our proposed fully unsupervised MirrorWiC models obtain substantial gains over off-the-shelf PLMs across all monolingual, multilingual and cross-lingual setups. Moreover, on some standard WiC benchmarks, MirrorWiC is even on-par with supervised models fine-tuned with in-task data and sense labels. ... : CoNLL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2109.09237 https://arxiv.org/abs/2109.09237 BASE Hide details 6 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... Liu, Qianchu; Liu, Fangyu; Collier, Nigel. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 7 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 8 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 9 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 10 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

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