Knowledge Commons of Institute of Automation,CAS
Select the Best Translation from Different Systems Without Reference | |
Lu JL(陆金梁)1,2![]() ![]() | |
2019-09 | |
会议名称 | NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING (NLPCC 2019) |
会议日期 | 2019-9 |
会议地点 | 中国,敦煌 |
摘要 | In recent years, neural machine translation (NMT) has made great progress. Different models, such as neural networks using recurrence, convolution and self-attention, have been proposed and various online translation systems can be available. It becomes a big challenge on how to choose the best translation among different systems. In this paper, we attempt to tackle this task and it can be intuitively considered as the Quality Estimation (QE) problem that requires enough humanannotated data in which each translation hypothesis is scored by human. However, we do not have rich data with high-quality human annotations in practice. To solve this problem, we resort to bilingual training data and propose a new method of mixed MT metrics to automatically score the translation hypotheses from different systems with their references so as to construct the pseudo human-annotated data. Based on the pseudo training data, we further design a novel QE model based on Multi-BERT and Bi-RNN with a joint-encoding strategy. Extensive experiments demonstrate that our proposed method can achieve promising results for the task to select the best translation from various systems. |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57385 |
专题 | 紫东太初大模型研究中心 |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing, China 2.National Laboratory of Pattern Recognition, CASIA, Beijing, Chia |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Lu JL,Zhang JJ. Select the Best Translation from Different Systems Without Reference[C],2019. |
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229.pdf(582KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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