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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 – 16 of 16 1 Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ... Litschko, Robert; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2022 BASE Show details 2 On cross-lingual retrieval with multilingual text encoders Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer Science + Business Media, 2022 BASE Show details 3 On Cross-Lingual Retrieval with Multilingual Text Encoders ... Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : arXiv, 2021 BASE Show details 4 Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ... Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : arXiv, 2021 BASE Show details 5 Evaluating multilingual text encoders for unsupervised cross-lingual retrieval Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer, 2021 BASE Show details 6 Probing Pretrained Language Models for Lexical Semantics ... Vulić, Ivan; Ponti, Edoardo Maria; Litschko, Robert. - : arXiv, 2020 BASE Show details 7 Probing Pretrained Language Models for Lexical Semantics ... Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 8 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 9 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 10 Probing pretrained language models for lexical semantics Vulić, Ivan; Korhonen, Anna; Litschko, Robert. - : Association for Computational Linguistics, 2020 BASE Show details 11 Towards instance-level parser selection for cross-lingual transfer of dependency parsers Litschko, Robert; Vulić, Ivan; Agić, Želiko. - : Association for Computational Linguistics, 2020 BASE Show details 12 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 13 How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions Glavaš, Goran; Litschko, Robert; Ruder, Sebastian. - : Association for Computational Linguistics, 2019 BASE Show details 14 Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ... Litschko, Robert; Glavaš, Goran; Ponzetto, Simone Paolo; Vulić, Ivan. - : arXiv, 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 ... : accepted at SIGIR'18 (preprint) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1805.00879 https://dx.doi.org/10.48550/arxiv.1805.00879 BASE Hide details 15 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 16 Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo. - : ACM, 2018. : ACM/SIGIR PROCEEDINGS 2018, 2018 BASE Show details

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