首页> 外文会议>Conference on empirical methods in natural language processing >Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval
【24h】

Learning to Translate: A Query-Specific Combination Approach for Cross-Lingual Information Retrieval

机译:学习翻译:跨语言信息检索的特定于查询的组合方法

获取原文
获取外文期刊封面目录资料

摘要

When documents and queries are presented in different languages, the common approach is to translate the query into the document language. While there are a variety of query translation approaches, recent research suggests that combining multiple methods into a single "structured query" is the most effective. In this paper, we introduce a novel approach for producing a unique combination recipe for each query, as it has also been shown that the optimal combination weights differ substantially across queries and other task specifics. Our query-specific combination method generates statistically significant improvements over other combination strategies presented in the literature, such as uniform and task-specific weighting. An in-depth empirical analysis presents insights about the effect of data size, domain differences, labeling and tuning on the end performance of our approach.
机译:当文档和查询以不同的语言显示时,常用的方法是将查询翻译成文档语言。尽管存在各种各样的查询翻译方法,但最近的研究表明,将多种方法组合到单个“结构化查询”中是最有效的。在本文中,我们介绍了一种新颖的方法,可为每个查询生成唯一的组合配方,因为还表明,最佳组合权重在查询和其他特定任务之间存在很大差异。我们的查询特定组合方法比文献中提出的其他组合策略(例如统一和特定于任务的加权)产生了统计上显着的改进。深入的实证分析提供了有关数据大小,域差异,标签和调整对我们方法的最终性能的影响的见解。

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