Titlebook: Machine Translation; 18th China Conferenc Tong Xiao,Juan Pino Conference proceedings 2022 The Editor(s) (if applicable) and The Author(s),

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,Optimizing Deep Transformers for?Chinese-Thai Low-Resource Translation,xplore the experiment settings (including the number of BPE merge operations, dropout probability, embedding size, etc.) for the low-resource scenario with the 6-layer Transformer. Considering that increasing the number of layers also increases the regularization on new model parameters (dropout mod

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CCMT 2022 Translation Quality Estimation Task,fort estimation in the 18th China Conference on Machine Translation (CCMT) 2022. This method is based on a predictor-estimator model. The predictor is an XLM-RoBERTa model pre-trained on a large-scale parallel corpus and extracts features from the source language text and machine-translated text. Th

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,Effective Data Augmentation Methods for?CCMT 2022,slation (CCMT 2022) evaluation tasks. We submitted the results of two bilingual machine translation (MT) evaluation tasks in CCMT 2022. One is Chinese-English bilingual MT tasks from the news field, the other is Chinese-Thai bilingual MT tasks in low resource languages. Our system is based on Transf

ostensible

,NJUNLP’s Submission for CCMT 2022 Quality Estimation Task, CCMT 2022 quality estimation sentence-level task for English-to-Chinese (EN-ZH). We follow the DirectQE framework, whose target is bridging the gap between pre-training on parallel data and fine-tuning on QE data. We further combine DirectQE with the pre-trained language model XLM-RoBERTa (XLM-R) w

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,ISTIC’s Thai-to-Chinese Neural Machine Translation System for?CCMT’ 2022,hina (ISTIC) for the 18th China Conference on Machine Translation (CCMT’ 2022). ISTIC participated in a low resource evaluation task: Thai-to-Chinese MT task. The paper mainly illuminates its system framework based on Transformer, data preprocessing methods and some strategies adopted in this system

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Pengcong Wang,Hongxu Hou,Shuo Sun,Nier Wu,Weichen Jian,Zongheng Yang,Yisong Wang‘ several years of experience in teaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of th

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