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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 – 9 of 9 1 EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification ... Spithourakis, Georgios P.; Vulić, Ivan; Lis, Michał. - : arXiv, 2022 BASE Show details 2 Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ... Litschko, Robert; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2022 BASE Show details 3 IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ... Bugliarello, Emanuele; Liu, Fangyu; Pfeiffer, Jonas. - : arXiv, 2022 BASE Show details 4 Verb Knowledge Injection for Multilingual Event Processing ... Majewska, Olga; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2020 BASE Show details 5 How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions ... Glavas, Goran; Litschko, Robert; Ruder, Sebastian; Vulic, Ivan. - : arXiv, 2019 Abstract: Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning and facilitate cross-lingual transfer of NLP models. Despite their ubiquitous usage in downstream tasks, recent increasingly popular projection-based CLE models are almost exclusively evaluated on a single task only: bilingual lexicon induction (BLI). Even BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models. In this work, we make the first step towards a comprehensive evaluation of cross-lingual word embeddings. We thoroughly evaluate both supervised and unsupervised CLE models on a large number of language pairs in the BLI task and three downstream tasks, providing new insights concerning the ability of cutting-edge CLE models to support cross-lingual NLP. We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI can result in deteriorated downstream performance. We indicate the ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1902.00508 https://dx.doi.org/10.48550/arxiv.1902.00508 BASE Hide details 6 Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ... Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2018 BASE Show details 7 A Survey Of Cross-lingual Word Embedding Models ... Ruder, Sebastian; Vulić, Ivan; Søgaard, Anders. - : arXiv, 2017 BASE Show details 8 Decoding Sentiment from Distributed Representations of Sentences ... Ponti, Edoardo Maria; Vulić, Ivan; Korhonen, Anna. - : arXiv, 2017 BASE Show details 9 Survey on the Use of Typological Information in Natural Language Processing ... O'Horan, Helen; Berzak, Yevgeni; Vulić, Ivan. - : arXiv, 2016 BASE Show details

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