Metadata records machine translation combining multi‐engine outputs with limited parallel data
Metadata records machine translation combining multi‐engine outputs with limited parallel data
复制标题
元数据记录结合多引擎输出和有限并行数据的机器翻译
DOI：
10.1002/asi.23925
复制
发表时间：
2018
影响因子：
3.5
通讯作者：
Xinyue Wang
中科院分区：
文献类型：
--
作者：
B. R. Ayala;Ryan Knudson;Jiangping Chen;Gaohui Cao;Xinyue Wang
利用机器翻译技术生成多语种元数据记录是促进数字图书馆多语种信息访问的一种方法。目前的在线翻译服务是可用的，而且价格合理，但对于创建多语言元数据记录并不总是有效的。在这项研究中，我们实施了3种不同的机器翻译策略，并评估了它们在将英文元数据记录翻译成中文和西班牙语时的表现。这些策略包括将来自3个在线机器翻译系统（b谷歌、Bing和Yahoo!）的机器翻译结果与额外的语言资源（如手动生成的平行语料库）和从国际合作伙伴获得的两种目标语言的元数据记录相结合。采用开源统计MT平台Moses设计和实现三种翻译策略。使用充分性和流畅性对机器翻译结果进行的人类评估表明，两种策略比两种语言的单独在线机器翻译系统产生更高质量的翻译。特别是，添加小的、手工生成的元数据记录的并行语料库可以显著提高翻译性能。我们的研究提出了一种有效和高效的机器翻译方法来为数字馆藏提供多语言服务。
One way to facilitate Multilingual Information Access (MLIA) for digital libraries is to generate multilingual metadata records by applying Machine Translation (MT) techniques. Current online MT services are available and affordable, but are not always effective for creating multilingual metadata records. In this study, we implemented 3 different MT strategies and evaluated their performance when translating English metadata records to Chinese and Spanish. These strategies included combining MT results from 3 online MT systems (Google, Bing, and Yahoo!) with and without additional linguistic resources, such as manually‐generated parallel corpora, and metadata records in the two target languages obtained from international partners. The open‐source statistical MT platform Moses was applied to design and implement the three translation strategies. Human evaluation of the MT results using adequacy and fluency demonstrated that two of the strategies produced higher quality translations than individual online MT systems for both languages. Especially, adding small, manually‐generated parallel corpora of metadata records significantly improved translation performance. Our study suggested an effective and efficient MT approach for providing multilingual services for digital collections.