| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996465345603316 |
|
|
Titolo |
Machine translation : 16th China Conference, CCMT 2020, Hohhot, China, October 10-12, 2020, revised selected papers / / Junhui Li, Andy Way (editors) |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Gateway East, Singapore : , : Springer, , [2020] |
|
©2020 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2020.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XII, 143 p. 14 illus.) |
|
|
|
|
|
|
Collana |
|
Communications in computer and information science ; ; 1328 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
|
|
|
|
|
Nota di contenuto |
|
Transfer Learning for Chinese-Lao Neural Machine Translation with Linguistic Similarity -- MTNER: A Corpus for Mongolian Tourism Named Entity Recognition -- Unsupervised Machine Translation Quality Estimation in Black-box Setting -- YuQ: A Chinese-Uyghur Medical-domain Neural Machine Translation Dataset Towards Knowledge-driven -- Quality Estimation for Machine Translation with Multi-granularity Interaction -- Transformer-based unified neural network for quality estimation and Transformer-based re-decoding model for machine translation -- NJUNLP’s Machine Translation System for CCMT-2020 Uighur → Chinese Translation Task -- Description and Findings of OPPO’s Machine Translation Systems for CCMT 2020 -- Tsinghua University Neural Machine Translation Systems for CCMT 2020 -- BJTU’s Submission to CCMT 2020 Quality Estimation Task -- NJUNLP’s Submission for CCMT20 Quality Estimation Task -- Tencent Submissions for the CCMT 2020 Quality Estimation Task -- Neural Machine Translation based on Back-Translation for Multilingual Translation Evaluation Task. . |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This book constitutes the refereed proceedings of the 16th China Conference on Machine Translation, CCMT 2020, held in Hohhot, China, in October 2020. The 13 papers presented in this volume were carefully reviewed and selected from 78 submissions and focus on all aspects of machine translation, including preprocessing, neural |
|
|
|
|
|
|
|
|
|
|
machine translation models, hybrid model, evaluation method, and post-editing. |
|
|
|
|
|
| |