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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 3 4 Hits 1 – 20 of 68 1 Improving Word Translation via Two-Stage Contrastive Learning ... Li, Yaoyiran; Liu, Fangyu; Collier, Nigel. - : arXiv, 2022 BASE Show details 2 Plan-then-Generate: Controlled Data-to-Text Generation via Planning ... Su, Yixuan; Vandyke, David; Wang, Sihui. - : arXiv, 2021 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; Collier, Nigel. - : arXiv, 2021 Abstract: Injecting external domain-specific knowledge (e.g., UMLS) into pretrained language models (LMs) advances their capability to handle specialised in-domain tasks such as biomedical entity linking (BEL). However, such abundant expert knowledge is available only for a handful of languages (e.g., English). In this work, by proposing a novel cross-lingual biomedical entity linking task (XL-BEL) and establishing a new XL-BEL benchmark spanning 10 typologically diverse languages, we first investigate the ability of standard knowledge-agnostic as well as knowledge-enhanced monolingual and multilingual LMs beyond the standard monolingual English BEL task. The scores indicate large gaps to English performance. We then address the challenge of transferring domain-specific knowledge in resource-rich languages to resource-poor ones. To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge ... : ACL-IJCNLP 2021 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2105.14398 https://arxiv.org/abs/2105.14398 BASE Hide details 5 MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ... Liu, Qianchu; Liu, Fangyu; Collier, Nigel. - : arXiv, 2021 BASE Show 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 Visually Grounded Reasoning across Languages and Cultures ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bugliarello, Emanuele; Collier, Nigel. - : Underline Science Inc., 2021 BASE Show details 8 Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ... Liu, Fangyu; Vulić, I; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 9 Visually Grounded Reasoning across Languages and Cultures ... Liu, Fangyu; Bugliarello, Emanuele; Ponti, Edoardo Maria. - : arXiv, 2021 BASE Show details 10 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 11 Self-Alignment Pretraining for Biomedical Entity Representations Liu, Fangyu; Shareghi, Ehsan; Meng, Zaiqiao. - : Association for Computational Linguistics, 2021. : Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021 BASE Show details 12 Large-scale exploration of neural relation classification architectures ... Le, HQ; Can, DC; Vu, ST. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 13 Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter ... Conforti, Costanza; Berndt, Jakob; Pilehvar, Mohammad Taher. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 14 A pragmatic guide to geoparsing evaluation Gritta, Milan; Pilehvar, Mohammad Taher; Collier, Nigel. - : Springer Netherlands, 2020. : Language Resources and Evaluation, 2020 BASE Show details 15 Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter Conforti, Costanza; Berndt, Jakob; Pilehvar, Mohammad Taher. - : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020 BASE Show details 16 STANDER: An expert-annotated dataset for news stance detection and evidence retrieval Conforti, C; Berndt, J; Pilehvar, MT. - : Association for Computational Linguistics, 2020. : Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020, 2020 BASE Show details 17 Large-scale exploration of neural relation classification architectures Le, HQ; Can, DC; Vu, ST. - : Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 2020 BASE Show details 18 A pragmatic guide to geoparsing evaluation : Toponyms, Named Entity Recognition and pragmatics [<Journal>] Gritta, Milan [Verfasser]; Pilehvar, Mohammad Taher [Verfasser]; Collier, Nigel [Verfasser] DNB Subject Category Language Show details 19 A Pragmatic Guide to Geoparsing Evaluation ... Gritta, Milan; Collier, Nigel; Pilehvar, Mohammad. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 20 A pragmatic guide to geoparsing evaluation ... Gritta, Milan; Pilehvar, Mohammad Taher; Collier, Nigel. - : Apollo - University of Cambridge Repository, 2019 BASE Show details Page: 1 2 3 4

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