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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 LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models ... Gong, Hongyu; Chaudhary, Vishrav; Tang, Yuqing. - : arXiv, 2021 BASE Show details 3 Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications ... Sun, Shuo; El-Kishky, Ahmed; Chaudhary, Vishrav. - : arXiv, 2021 BASE Show details 4 Few-shot Learning with Multilingual Language Models ... Lin, Xi Victoria; Mihaylov, Todor; Artetxe, Mikel. - : arXiv, 2021 BASE Show details 5 Alternative Input Signals Ease Transfer in Multilingual Machine Translation ... Sun, Simeng; Fan, Angela; Cross, James; Chaudhary, Vishrav; Tran, Chau; Koehn, Philipp; Guzman, Francisco. - : arXiv, 2021 Abstract: Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages, particularly cases where higher-resourced languages can benefit lower-resourced ones. While training an MMT model, the supervision signals learned from one language pair can be transferred to the other via the tokens shared by multiple source languages. However, the transfer is inhibited when the token overlap among source languages is small, which manifests naturally when languages use different writing systems. In this paper, we tackle inhibited transfer by augmenting the training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. We test these signals on Indic and Turkic languages, two language families where the writing systems differ but languages still share common features. Our results indicate that a straightforward multi-source self-ensemble -- training a model on a mixture of various signals and ensembling ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2110.07804 https://arxiv.org/abs/2110.07804 BASE Hide 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 AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages ... Ebrahimi, Abteen; Mager, Manuel; Oncevay, Arturo. - : arXiv, 2021 BASE Show details 8 Multilingual Translation with Extensible Multilingual Pretraining and Finetuning ... Tang, Yuqing; Tran, Chau; Li, Xian. - : arXiv, 2020 BASE Show details 9 MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset ... Fomicheva, Marina; Sun, Shuo; Fonseca, Erick. - : 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 WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia ... Schwenk, Holger; Chaudhary, Vishrav; Sun, Shuo. - : arXiv, 2019 BASE Show details

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