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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 – 7 of 7 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 Delving Deeper into Cross-lingual Visual Question Answering ... Liu, Chen; Pfeiffer, Jonas; Korhonen, Anna. - : arXiv, 2022 BASE Show details 3 Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval ... Litschko, Robert; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2022 BASE Show details 4 IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ... Bugliarello, Emanuele; Liu, Fangyu; Pfeiffer, Jonas; Reddy, Siva; Elliott, Desmond; Ponti, Edoardo Maria; Vulić, Ivan. - : arXiv, 2022 Abstract: Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, vision-and-language research has mostly focused on English language tasks. To fill this gap, we introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together - by both aggregating pre-existing datasets and creating new ones - visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups. Based on the evaluation of the available state-of-the-art models, we find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks. Moreover, downstream performance is partially explained by the amount of ... Keyword: Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2201.11732 https://arxiv.org/abs/2201.11732 BASE Hide details 5 Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation ... Majewska, Olga; Razumovskaia, Evgeniia; Ponti, Edoardo Maria. - : arXiv, 2022 BASE Show details 6 Improving Word Translation via Two-Stage Contrastive Learning ... Li, Yaoyiran; Liu, Fangyu; Collier, Nigel. - : arXiv, 2022 BASE Show details 7 On cross-lingual retrieval with multilingual text encoders Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer Science + Business Media, 2022 BASE Show details

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