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

invigorating

书目名称Machine Translation影响因子(影响力)




书目名称Machine Translation影响因子(影响力)学科排名




书目名称Machine Translation网络公开度




书目名称Machine Translation网络公开度学科排名




书目名称Machine Translation被引频次




书目名称Machine Translation被引频次学科排名




书目名称Machine Translation年度引用




书目名称Machine Translation年度引用学科排名




书目名称Machine Translation读者反馈




书目名称Machine Translation读者反馈学科排名

暖昧关系

CCMT 2022 Translation Quality Estimation Task, found that pre-training the predictor with the semantic textual similarity (STS) task in the parallel corpus and using augmented training data constructed by different machine translation (MT) engines can improve the prediction effect of the Human-targeted Translation Edit Rate (HTER) in both Chinese-English and English-Chinese tasks.

装勇敢地做

爵士乐

先行

,Multi-strategy Enhanced Neural Machine Translation for Chinese Minority Languages,d Ensemble. Our enhancement experiments have proved the effectiveness of above-mentioned strategies. We submit enhanced systems as primary systems for the three tracks. In addition, we train contrast models using additional bilingual data and submit results generated by these contrast models.

Arthropathy

,An Improved Multi-task Approach to Pre-trained Model Based MT Quality Estimation,r model. We show that the post-editing sub-task is much more in-formative and the mBART is superior to other pre-trained models. Experiments on WMT2021 English-German and English-Chinese QE datasets showed that the proposed method achieves 1.2%–2.1% improvements in the strong sentence-level QE baseline.

卡死偷电

值得赞赏

,Effective Data Augmentation Methods for CCMT 2022,ormer model with several effective data augmentation strategies which are adopted to improve the quality of translation. Experiments show that data augmentation methods have a good impact on the baseline system and aim to enhance the robustness of the model.

IST

,NJUNLP’s Submission for CCMT 2022 Quality Estimation Task,hich achieves outstanding success in many NLP tasks in order to improve performance. With the purpose of better utilizing parallel data, several types of pseudo data are employed in our method as well. In addition, we also ensemble several models to promote the final results.

挑剔为人