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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 – 16 of 16 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 GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems ... Ding, Bosheng; Hu, Junjie; Bing, Lidong. - : arXiv, 2021 BASE Show details 5 DEEP: DEnoising Entity Pre-training for Neural Machine Translation ... Hu, Junjie; Hayashi, Hiroaki; Cho, Kyunghyun; Neubig, Graham. - : arXiv, 2021 Abstract: It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation methods mainly focus on phonetic transliteration, which ignores the sentence context for translation and is limited in domain and language coverage. To address this limitation, we propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences. Besides, we investigate a multi-task learning strategy that finetunes a pre-trained neural machine translation model on both entity-augmented monolingual data and parallel data to further improve entity translation. Experimental results on three language pairs demonstrate that \method results in significant improvements over strong denoising auto-encoding baselines, with a gain of up to 1.3 BLEU and up to 9.2 entity accuracy points for English-Russian translation. ... : 13 pages ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2111.07393 https://arxiv.org/abs/2111.07393 BASE Hide details 6 Explicit Alignment Objectives for Multilingual Bidirectional Encoders ... NAACL 2021 2021; Firat, Orhan; Hu, Junjie. - : Underline Science Inc., 2021 BASE Show details 7 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 8 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 9 Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting ... Huang, Po-Yao; Hu, Junjie; Chang, Xiaojun. - : arXiv, 2020 BASE Show details 10 Explicit Alignment Objectives for Multilingual Bidirectional Encoders ... Hu, Junjie; Johnson, Melvin; Firat, Orhan. - : arXiv, 2020 BASE Show details 11 On Learning Language-Invariant Representations for Universal Machine Translation ... Zhao, Han; Hu, Junjie; Risteski, Andrej. - : arXiv, 2020 BASE Show details 12 XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization ... Hu, Junjie; Ruder, Sebastian; Siddhant, Aditya. - : arXiv, 2020 BASE Show details 13 Domain Adaptation of Neural Machine Translation by Lexicon Induction ... Hu, Junjie; Xia, Mengzhou; Neubig, Graham. - : arXiv, 2019 BASE Show details 14 Rapid Adaptation of Neural Machine Translation to New Languages ... Neubig, Graham; Hu, Junjie. - : arXiv, 2018 BASE Show details 15 Structural Embedding of Syntactic Trees for Machine Comprehension ... Liu, Rui; Hu, Junjie; Wei, Wei. - : arXiv, 2017 BASE Show details 16 Learning Lexical Entries for Robotic Commands using Crowdsourcing ... Hu, Junjie; Oh, Jean; Gershman, Anatole. - : arXiv, 2016 BASE Show details

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