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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 3 4 5...7 Hits 1 – 20 of 129 1 Delving Deeper into Cross-lingual Visual Question Answering ... Liu, Chen; Pfeiffer, Jonas; Korhonen, Anna. - : arXiv, 2022 BASE Show details 2 Combating Temporal Drift in Crisis with Adapted Embeddings ... Stowe, Kevin; Gurevych, Iryna. - : arXiv, 2021 BASE Show details 3 Annotation Curricula to Implicitly Train Non-Expert Annotators ... Lee, Ji-Ung; Klie, Jan-Christoph; Gurevych, Iryna. - : arXiv, 2021 BASE Show details 4 Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ... Ribeiro, Leonardo F. R.; Pfeiffer, Jonas; Zhang, Yue. - : arXiv, 2021 BASE Show details 5 xGQA: Cross-Lingual Visual Question Answering ... Pfeiffer, Jonas; Geigle, Gregor; Kamath, Aishwarya. - : arXiv, 2021 BASE Show details 6 BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models ... Thakur, Nandan; Reimers, Nils; Rücklé, Andreas. - : arXiv, 2021 BASE Show details 7 Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning ... Utama, Prasetya Ajie; Moosavi, Nafise Sadat; Sanh, Victor. - : arXiv, 2021 BASE Show details 8 Metaphor Generation with Conceptual Mappings ... Stowe, Kevin; Chakrabarty, Tuhin; Peng, Nanyun. - : arXiv, 2021 BASE Show details 9 GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval ... Wang, Kexin; Thakur, Nandan; Reimers, Nils. - : arXiv, 2021 BASE Show details 10 Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs Ribeiro, Leonardo,; Zhang, Yue; Gardent, Claire... In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03020314 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2020, 8, ⟨10.1162/tacl_a_00332⟩ (2020) BASE Show details 11 Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation ... Reimers, Nils; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 12 How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation ... Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 13 MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 14 Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning ... Miller, Tristan; Dinh, Erik-Lân Do; Simpson, Edwin. - : arXiv, 2020 BASE Show details 15 How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ... Rust, Phillip; Pfeiffer, Jonas; Vulić, Ivan; Ruder, Sebastian; Gurevych, Iryna. - : arXiv, 2020 Abstract: In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a set of nine typologically diverse languages with readily available pretrained monolingual models on a set of five diverse monolingual downstream tasks. We first aim to establish, via fair and controlled comparisons, if a gap between the multilingual and the corresponding monolingual representation of that language exists, and subsequently investigate the reason for any performance difference. To disentangle conflating factors, we train new monolingual models on the same data, with monolingually and multilingually trained tokenizers. We find that while the pretraining data size is an important factor, a designated monolingual tokenizer plays an equally important role in the downstream performance. Our results show that languages that are adequately represented in the multilingual model's ... : ACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2012.15613 https://dx.doi.org/10.48550/arxiv.2012.15613 BASE Hide details 16 UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ... Pfeiffer, Jonas; Vulić, Ivan; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 17 PuzzLing Machines: A Challenge on Learning From Small Data ... Şahin, Gözde Gül; Kementchedjhieva, Yova; Rust, Phillip. - : arXiv, 2020 BASE Show details 18 A Matter of Framing: The Impact of Linguistic Formalism on Probing Results ... Kuznetsov, Ilia; Gurevych, Iryna. - : arXiv, 2020 BASE Show details 19 Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs ... Ribeiro, Leonardo F. R.; Zhang, Yue; Gardent, Claire. - : arXiv, 2020 BASE Show details 20 Empowering Active Learning to Jointly Optimize System and User Demands ... Lee, Ji-Ung; Meyer, Christian M.; Gurevych, Iryna. - : arXiv, 2020 BASE Show details Page: 1 2 3 4 5...7

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