• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Workshop on discourse in machine translation >Detecting Narrativity to Improve English to French Translation of Simple Past Verbs
【24h】

Detecting Narrativity to Improve English to French Translation of Simple Past Verbs

机译：检测叙事，以提高英语到法语翻译简单过去动词

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The correct translation of verb tenses ensures that the temporal ordering of events in the source text is maintained in the target text. This paper assesses the utility of automatically labeling English Simple Past verbs with a binary discursive feature, narrative vs. non-narrative, for statistical machine translation (SMT) into French. The narrativity feature, which helps deciding which of the French past tenses is a correct translation of the English Simple Past, can be assigned with about 70% accuracy (F1). The narrativity feature improves SMT by about 0.2 BLEU points when a factored SMT system is trained and tested on automatically labeled English-French data. More importantly, manual evaluation shows that verb tense translation and verb choice are improved by respectively 9.7% and 3.4% (absolute), leading to an overall improvement of verb translation of 17% (relative).
机译：动词时态的正确转换确保了源文本中的事件的时间顺序维护在目标文本中。本文评估了使用二进制话语特征，叙述与非叙述，统计机器翻译（SMT）进入法语的二进制话语特征，自动标记英语简单的过去动词的效用。叙事特征有助于决定哪个法国过去时态是英语简单过去的正确翻译，可以分配大约70％的精度（F1）。当经过考虑的SMT系统培训并在自动标记的英语 - 法语数据上进行培训并测试时，叙事功能将SMT提高约0.2个BLEU点。更重要的是，手动评估表明，动词时态翻译和动词选择分别得到9.7％和3.4％（绝对），导致动词翻译的总体改善了17％（相对）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号