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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 – 12 of 12 1 The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation ... Goyal, Naman; Gao, Cynthia; Chaudhary, Vishrav. - : arXiv, 2021 BASE Show details 2 Multilingual Autoregressive Entity Linking ... De Cao, Nicola; Wu, Ledell; Popat, Kashyap. - : arXiv, 2021 BASE Show details 3 Larger-Scale Transformers for Multilingual Masked Language Modeling ... Goyal, Naman; Du, Jingfei; Ott, Myle. - : arXiv, 2021 BASE Show details 4 FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task ... Tang, Yun; Gong, Hongyu; Li, Xian. - : arXiv, 2021 BASE Show details 5 Few-shot Learning with Multilingual Language Models ... Lin, Xi Victoria; Mihaylov, Todor; Artetxe, Mikel. - : arXiv, 2021 BASE Show details 6 Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ... Ko, Wei-Jen; El-Kishky, Ahmed; Renduchintala, Adithya. - : arXiv, 2021 BASE Show details 7 Multilingual Translation with Extensible Multilingual Pretraining and Finetuning ... Tang, Yuqing; Tran, Chau; Li, Xian. - : arXiv, 2020 BASE Show details 8 Facebook AI's WMT20 News Translation Task Submission ... Chen, Peng-Jen; Lee, Ann; Wang, Changhan; Goyal, Naman; Fan, Angela; Williamson, Mary; Gu, Jiatao. - : arXiv, 2020 Abstract: This paper describes Facebook AI's submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil English and Inuktitut English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two main strategies, leveraging all available data and adapting the system to the target news domain. We explore techniques that leverage bitext and monolingual data from all languages, such as self-supervised model pretraining, multilingual models, data augmentation, and reranking. To better adapt the translation system to the test domain, we explore dataset tagging and fine-tuning on in-domain data. We observe that different techniques provide varied improvements based on the available data of the language pair. Based on the finding, we integrate these techniques into one training pipeline. For En->Ta, we explore an unconstrained setup with additional Tamil bitext and monolingual data and show that ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2011.08298 https://arxiv.org/abs/2011.08298 BASE Hide details 9 Multilingual Denoising Pre-training for Neural Machine Translation ... Liu, Yinhan; Gu, Jiatao; Goyal, Naman. - : arXiv, 2020 BASE Show details 10 Beyond English-Centric Multilingual Machine Translation ... Fan, Angela; Bhosale, Shruti; Schwenk, Holger. - : arXiv, 2020 BASE Show details 11 Unsupervised Cross-lingual Representation Learning at Scale ... Conneau, Alexis; Khandelwal, Kartikay; Goyal, Naman. - : arXiv, 2019 BASE Show details 12 The Social Dynamics of Language Change in Online Networks ... Goel, Rahul; Soni, Sandeep; Goyal, Naman. - : arXiv, 2016 BASE Show details

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