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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 21 1 XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages ... Glavas, Goran; Karan, Mladen; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 2 From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers ... Lauscher, Anne; Ravishankar, Vinit; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 3 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ... Lauscher, Anne; Vulic, Ivan; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 4 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ... Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 5 Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces ... Glavas, Goran; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 6 Probing Pretrained Language Models for Lexical Semantics ... Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 7 Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ... Karan, Mladen; Vulic, Ivan; Korhonen, Anna. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 8 Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ... Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 9 Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : 5TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2020), 2020 BASE Show details 10 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity Lauscher, Anne; Vulic, Ivan; Ponti, Edoardo. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.118, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020 BASE Show details 11 Probing Pretrained Language Models for Lexical Semantics Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 12 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.2, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020 BASE Show details 13 Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction Karan, Mladen; Vulic, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2020 BASE Show details 14 Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers Glavas, Goran; Agic, Zeljko; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.345, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020 BASE Show details 15 From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers Ravishankar, Vinit; Glavas, Goran; Lauscher, Anne. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 16 XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages Glavas, Goran; Karan, Mladen; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.559, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020 BASE Show details 17 How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ... Glavas, Goran; Litschko, Robert; Ruder, Sebastian. - : arXiv, 2019 BASE Show details 18 Specialising Distributional Vectors of All Words for Lexical Entailment ... Kamath, Aishwarya; Pfeiffer, Jonas; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 19 Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ... Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 20 Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo; Vulic, Ivan; SIGIR, ACM. - : ACM, 2018. : ACM/SIGIR PROCEEDINGS 2018, 2018 Abstract: We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all. The framework leverages shared cross-lingual word embedding spaces in which terms, queries, and documents can be represented, irrespective of their actual language. The shared embedding spaces are induced solely on the basis of monolingual corpora in two languages through an iterative process based on adversarial neural networks. Our experiments on the standard CLEF CLIR collections for three language pairs of varying degrees of language similarity (English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed fully unsupervised approach. Our CLIR models with unsupervised cross-lingual embeddings outperform baselines that utilize cross-lingual embeddings induced relying on word-level and document-level alignments. We then demonstrate that further improvements can be achieved by unsupervised ensemble CLIR models. We believe that the proposed framework is the first step towards development of effective CLIR models for language pairs and domains where parallel data are scarce or non-existent. Keyword: cross-lingual vector spaces; Unsupervised cross-lingual IR URL: https://www.repository.cam.ac.uk/handle/1810/279400 https://doi.org/10.17863/CAM.26775 BASE Hide details Page: 1 2

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