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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 – 11 of 11 1 Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ... Huang, Po-Yao; Patrick, Mandela; Hu, Junjie. - : arXiv, 2021 BASE Show details 2 XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ... Ruder, Sebastian; Constant, Noah; Botha, Jan. - : arXiv, 2021 BASE Show details 3 AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages ... Reid, Machel; Hu, Junjie; Neubig, Graham. - : arXiv, 2021 BASE Show details 4 DEEP: DEnoising Entity Pre-training for Neural Machine Translation ... Hu, Junjie; Hayashi, Hiroaki; Cho, Kyunghyun. - : arXiv, 2021 BASE Show details 5 Explicit Alignment Objectives for Multilingual Bidirectional Encoders ... NAACL 2021 2021; Firat, Orhan; Hu, Junjie. - : Underline Science Inc., 2021 BASE Show details 6 Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ... NAACL 2021 2021; Hauptmann, Alexander; Hu, Junjie. - : Underline Science Inc., 2021 BASE Show details 7 Phrase-level Active Learning for Neural Machine Translation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hu, Junjie; Neubig, Graham. - : Underline Science Inc., 2021 BASE Show details 8 Explicit Alignment Objectives for Multilingual Bidirectional Encoders ... Hu, Junjie; Johnson, Melvin; Firat, Orhan; Siddhant, Aditya; Neubig, Graham. - : arXiv, 2020 Abstract: Pre-trained cross-lingual encoders such as mBERT (Devlin et al., 2019) and XLMR (Conneau et al., 2020) have proven to be impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resource languages. This success comes despite the fact that there is no explicit objective to align the contextual embeddings of words/sentences with similar meanings across languages together in the same space. In this paper, we present a new method for learning multilingual encoders, AMBER (Aligned Multilingual Bidirectional EncodeR). AMBER is trained on additional parallel data using two explicit alignment objectives that align the multilingual representations at different granularities. We conduct experiments on zero-shot cross-lingual transfer learning for different tasks including sequence tagging, sentence retrieval and sentence classification. Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on ... : published at NAACL 2021 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2010.07972 https://arxiv.org/abs/2010.07972 BASE Hide details 9 XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization ... Hu, Junjie; Ruder, Sebastian; Siddhant, Aditya. - : arXiv, 2020 BASE Show details 10 Domain Adaptation of Neural Machine Translation by Lexicon Induction ... Hu, Junjie; Xia, Mengzhou; Neubig, Graham. - : arXiv, 2019 BASE Show details 11 Rapid Adaptation of Neural Machine Translation to New Languages ... Neubig, Graham; Hu, Junjie. - : arXiv, 2018 BASE Show details

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