SBIR Phase II: NUMBERS: Bringing Statistical Machine Translation into the Real World

这个小型企业创新研究(SBIR)第二阶段项目的目标是为机器翻译市场带来全新的技术。虽然目前的系统是基于规则的，很难扩展，但该公司使用了一个统计系统，通过自动分析以前翻译的大量材料来学习翻译。这项技术的性能已经超过了基于规则的系统，而且它很容易适应感兴趣的特定领域，例如跨国公司生成的技术文档。该公司已授权(并共同开发)来自世界机器翻译领先者USC/ISI创始人研究团队的关键软件引擎。在这个项目中，他们将在客户需求的推动下，以三种方式扩展他们的统计引擎--他们建议建立(1)处理大量文本的并行、基于集群的培训系统，(2)翻译数字、日期、个人姓名、地点等的新功能(“命名实体”)，以及(3)快速定制工具，该工具将帮助定制翻译引擎，以满足特定客户领域的需求。情报机构、企业用户以及天使和风险投资集团一直对客户/资金感兴趣。在接下来的一年里，我们的计划是通过开发几个客户并发货第一批产品来利用这种兴趣。

The goal of this Small Business Innovation Research (SBIR) Phase II project is to bring radically new technology to the machine translation marketplace. While current systems are rule-based and difficult to extend, this company employs a statistical system that learns to translate by automatically analyzing large collections of previously translated material. This technology already outperforms rule-based systems, and it easily adapts to specific domains of interest, such as technical documentation generated by multinational corporations. The company has licensed (and co-developed) key software engines from the founders' research team at USC/ISI, a world leader in machine translation. In this project, they will extend their statistical engine in three ways, driven by customer needs -- they propose to build (1) a parallel, cluster-based training system for handling large text volumes (2) new capabilities for translating numbers, dates, personal names, locations, etc. ("named entities"), and (3) rapid customization tools that will assist with customized translation engines for specific customer domain requirements.There has been substantial client/funding interest from intelligence agencies, corporate users, and angel and venture investment groups. Over the next year, the plan is to capitalize on this interest by developing several clients and shipping the first products.