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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 – 17 of 17 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 Data for paper: "Evaluating Resource-Lean Cross-Lingual Embedding Models in Unsupervised Retrieval" ... Litschko, Robert; Glavaš, Goran. - : Mannheim University Library, 2021 BASE Show details 4 On Cross-Lingual Retrieval with Multilingual Text Encoders ... 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. - : arXiv, 2021 BASE Show details 6 Evaluating multilingual text encoders for unsupervised cross-lingual retrieval Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer, 2021 BASE Show details 7 Probing Pretrained Language Models for Lexical Semantics ... Vulić, Ivan; Ponti, Edoardo Maria; Litschko, Robert. - : arXiv, 2020 BASE Show details 8 Probing Pretrained Language Models for Lexical Semantics ... Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 9 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 10 Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers Glavas, Goran; Agic, Zeljko; Vulic, Ivan; Litschko, Robert. - : 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 Abstract: Current methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, "at treebank level”. In this work, we propose and argue for a novel cross-lingual transfer paradigm: instance-level parser selection (ILPS), and present a proof-of-concept study focused on instance-level selection in the framework of delexicalized parser transfer. Our work is motivated by an empirical observation that different source parsers are the best choice for different Universal POS-sequences (i.e., UPOS sentences) in the target language. We then propose to predict the best parser at the instance level. To this end, we train a supervised regression model, based on the Transformer architecture, to predict parser accuracies for individual POS-sequences. We compare ILPS against two strong single-best parser selection baselines (SBPS): (1) a model that compares POS n-gram distributions between the source and target languages (KL) and (2) a model that selects the source based on the similarity between manually created language vectors encoding syntactic properties of languages (L2V). The results from our extensive evaluation, coupling 42 source parsers and 20 diverse low-resource test languages, show that ILPS outperforms KL and L2V on 13/20 and 14/20 test languages, respectively. Further, we show that by predicting the best parser “at treebank level” (SBPS), using the aggregation of predictions from our instance-level model, we outperform the same baselines on 17/20 and 16/20 test languages. URL: https://doi.org/10.17863/CAM.62214 https://www.repository.cam.ac.uk/handle/1810/315107 BASE Hide details 11 Probing pretrained language models for lexical semantics Vulić, Ivan; Korhonen, Anna; Litschko, Robert. - : Association for Computational Linguistics, 2020 BASE Show details 12 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 13 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 14 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 15 Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ... Litschko, Robert; Glavaš, Goran; Ponzetto, Simone Paolo. - : arXiv, 2018 BASE Show details 16 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 17 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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