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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 – 3 of 3 1 Universal Conditional Masked Language Pre-training for Neural Machine Translation ... Li, Pengfei; Li, Liangyou; Zhang, Meng. - : arXiv, 2022 BASE Show details 2 SimulSLT: End-to-End Simultaneous Sign Language Translation ... Yin, Aoxiong; Zhao, Zhou; Liu, Jinglin. - : arXiv, 2021 BASE Show details 3 Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training ... Wu, Minghao; Li, Yitong; Zhang, Meng; Li, Liangyou; Haffari, Gholamreza; Liu, Qun. - : arXiv, 2021 Abstract: Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in real world. One common practice is to adjust the share of each corpus in the training, so that the learning process is balanced and low-resource cases can benefit from the high resource ones. However, automatic balancing methods usually depend on the intra- and inter-dataset characteristics, which is usually agnostic or requires human priors. In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation. We experiments with two classes of uncertainty measures on multilingual (16 languages with 4 settings) and multi-domain settings (4 for in-domain and 2 for out-of-domain on English-German translation) and demonstrate our approach MultiUAT substantially outperforms its baselines, including both ... : 15 pages, 4 figures, to appear at EMNLP 2021 main conference ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2109.02284 https://arxiv.org/abs/2109.02284 BASE Hide details

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