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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 – 12 of 12 1 UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Iryna; ., Sebastian. - : Underline Science Inc., 2021 BASE Show details 2 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 3 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 4 SemEval-2020 Task 3: Graded Word Similarity in Context ... The 28th International Conference on Computational Linguistics 2020; Armendariz, Carlos; Ljubešić, Nikola. - : Underline Science Inc., 2020 BASE Show details 5 Emergent Communication Pretraining for Few-Shot Machine Translation ... The 28th International Conference on Computational Linguistics 2020; Korhonen, Anna; Li, Yaoyiran. - : Underline Science Inc., 2020 BASE Show details 6 Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ... The 28th International Conference on Computational Linguistics 2020; Korhonen, Anna; Majewska, Olga. - : Underline Science Inc., 2020 BASE Show details 7 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ... The 28th International Conference on Computational Linguistics 2020; Glavaš, Goran; Korhonen, Anna. - : Underline Science Inc., 2020 BASE Show details 8 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ... The 28th International Conference on Computational Linguistics 2020; Glavaš, Goran; Korhonen, Anna. - : Underline Science Inc., 2020 BASE Show details 9 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Ponti, Edoardo,; O 'horan, Helen; Berzak, Yevgeni; Vulic, Ivan; Reichart, Roi; Poibeau, Thierry; Shutova, Ekaterina; Korhonen, Anna In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018) Abstract: Addressing the cross-lingual variation of grammatical structures and meaning categorization is a key challenge for multilingual Natural Language Processing. The lack of resources for the majority of the world's languages makes supervised learning not viable. Moreover, the performance of most algorithms is hampered by language-specific biases and the neglect of informative multilingual data. The discipline of Linguistic Typology provides a principled framework to compare languages systematically and empirically and documents their variation in publicly available databases. These enshrine crucial information to design language-independent algorithms and refine techniques devised to mitigate the above-mentioned issues, including cross-lingual transfer and multilingual joint models, with typological features. In this survey, we demonstrate that typology is beneficial to several NLP applications, involving both semantic and syntactic tasks. Moreover, we outline several techniques to extract features from databases or acquire them automatically: these features can be subsequently integrated into multilingual models to tie parameters together cross-lingually or gear a model towards a specific language. Finally, we advocate for a new typology that accounts for the patterns within individual examples rather than entire languages, and for graded categories rather than discrete ones, in oder to bridge the gap with the contextual and continuous nature of machine learning algorithms. Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SCCO]Cognitive science; [SHS.INFO]Humanities and Social Sciences/Library and information sciences; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [SHS.STAT]Humanities and Social Sciences/Methods and statistics; Language typology; Machine learning; Natural Language Processing URL: https://hal.archives-ouvertes.fr/hal-01856176 https://hal.archives-ouvertes.fr/hal-01856176/document https://hal.archives-ouvertes.fr/hal-01856176/file/1807.00914.pdf BASE Hide details 10 A deep learning approach to bilingual lexicon induction in the biomedical domain. ... Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 11 A deep learning approach to bilingual lexicon induction in the biomedical domain. Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018 BASE Show details 12 Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine. Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan. - : BioMed Central, 2018. : BMC bioinformatics, 2018 BASE Show details

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