Titlebook: Machine Translation; 17th China Conferenc Jinsong Su,Rico Sennrich Conference proceedings 2021 Springer Nature Singapore Pte Ltd. 2021 arti

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tuted for the unknown true values. It is natural, then, to ask how the variance–covariance parameters should be estimated. Answering this question is the topic of this chapter. We begin with an answer that applies when the model is a components-of-variance model, for which a method known as quadrati

斷言

Robust

Sleep-Paralysis

intellect

協(xié)議

,SAU’S Submission for CCMT 2021 Quality Estimation Task,N-ZH). In this task. We follow TransQuest framework which is based on cross-lingual transformers (XLM-R). In order to make the model pay more attention to key words, we use the attention mechanism and gate module to fuse the last hidden state and pooler output of XLM-R model to generate more accurat

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Anthropoid

Low-Resource Neural Machine Translation Based on Improved Reptile Meta-learning Method, global optimal parameters obtained through transfer learning can not effectively adapt to new tasks, which means the problem of local optimum will be caused when training the new task model. Although this problem can be alleviated by optimization-based meta-learning methods, but meta-parameters are