-
公开(公告)号：US08886516B2
公开(公告)日：2014-11-11
申请号：US13409419
申请日：2012-03-01
IPC分类号： G06F17/28
CPC分类号： G06F17/289
摘要： A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.
摘要翻译： 一种翻译方法包括将源文本部分上传到后端处理器。 后端处理器识别与源文本部分相关联的翻译知识的子集。 后端处理器将子集下载到前端处理器。 翻译引擎在前端处理器上运行。 翻译引擎生成作为子集的函数的源文本部分的翻译。
-
公开(公告)号：US08504354B2
公开(公告)日：2013-08-06
申请号：US12131144
申请日：2008-06-02
CPC分类号： G06F17/2818 , G06F17/2827
摘要： Machine translation algorithms for translating between a first language and a second language are often trained using parallel fragments, comprising a first language corpus and a second language corpus comprising an element-for-element translation of the first language corpus. Such training may involve large training sets that may be extracted from large bodies of similar sources, such as databases of news articles written in the first and second languages describing similar events; however, extracted fragments may be comparatively “noisy,” with extra elements inserted in each corpus. Extraction techniques may be devised that can differentiate between “bilingual” elements represented in both corpora and “monolingual” elements represented in only one corpus, and for extracting cleaner parallel fragments of bilingual elements. Such techniques may involve conditional probability determinations on one corpus with respect to the other corpus, or joint probability determinations that concurrently evaluate both corpora for bilingual elements.
摘要翻译： 通常使用并行片段来训练用于在第一语言和第二语言之间进行翻译的机器翻译算法，包括第一语言语料库和包括第一语言语料库的元素元素翻译的第二语言语料库。 这种培训可能涉及可能从类似来源的大型机构中提取的大型训练集，例如描述类似事件的第一种和第二种语言的新闻文章数据库; 然而，提取的片段可能相对“嘈杂”，在每个语料库中插入额外的元素。 可以设计出可以区分在仅一个语料库中表示的语料库和“单语”元素中的“双语”元素的提取技术，以及用于提取双语元素的更清洁的平行片段。 这样的技术可以涉及一个语料库相对于另一个语料库的条件概率确定，或联合概率确定，其同时评价双语单元的语料库。
-
公开(公告)号：US20090299729A1
公开(公告)日：2009-12-03
申请号：US12131144
申请日：2008-06-02
IPC分类号： G06F17/27
CPC分类号： G06F17/2818 , G06F17/2827
摘要： Machine translation algorithms for translating between a first language and a second language are often trained using parallel fragments, comprising a first language corpus and a second language corpus comprising an element-for-element translation of the first language corpus. Such training may involve large training sets that may be extracted from large bodies of similar sources, such as databases of news articles written in the first and second languages describing similar events; however, extracted fragments may be comparatively “noisy,” with extra elements inserted in each corpus. Extraction techniques may be devised that can differentiate between “bilingual” elements represented in both corpora and “monolingual” elements represented in only one corpus, and for extracting cleaner parallel fragments of bilingual elements. Such techniques may involve conditional probability determinations on one corpus with respect to the other corpus, or joint probability determinations that concurrently evaluate both corpora for bilingual elements.
摘要翻译： 通常使用并行片段来训练用于在第一语言和第二语言之间进行翻译的机器翻译算法，包括第一语言语料库和包括第一语言语料库的元素元素翻译的第二语言语料库。 这种培训可能涉及可能从类似来源的大型机构中提取的大型训练集，例如描述类似事件的第一种和第二种语言的新闻文章数据库; 然而，提取的片段可能相对“嘈杂”，在每个语料库中插入额外的元素。 可以设计出可以区分在仅一个语料库中表示的语料库和“单语”元素中的“双语”元素的提取技术，以及用于提取双语元素的更清洁的平行片段。 这样的技术可以涉及一个语料库相对于另一个语料库的条件概率确定，或联合概率确定，其同时评价双语单元的语料库。
-
公开(公告)号：US07593843B2
公开(公告)日：2009-09-22
申请号：US10813208
申请日：2004-03-30
IPC分类号： G06F17/28
CPC分类号： G06F17/2818 , G06F17/2881
摘要： A method of decoding an input semantic structure to generate an output semantic structure. A set of transfer mappings are provided. A score is calculated for at least one transfer mapping in the set of transfer mappings using a statistical model. At least one transfer mapping is selected based on the score and used to construct the output semantic structure.
摘要翻译： 一种解码输入语义结构以生成输出语义结构的方法。 提供一组传输映射。 使用统计模型计算一组传输映射中的至少一个传输映射的分数。 基于分数选择至少一个传输映射，并用于构建输出语义结构。
-
5.发明授权System and method for machine learning a confidence metric for machine translation 有权用于机器学习机器翻译的置信度量的系统和方法公开(公告)号：US07496496B2
公开(公告)日：2009-02-24
申请号：US11725435
申请日：2007-03-19
CPC分类号： G06F17/28
摘要： A machine translation system is trained to generate confidence scores indicative of a quality of a translation result. A source string is translated with a machine translator to generate a target string. Features indicative of translation operations performed are extracted from the machine translator. A trusted entity-assigned translation score is obtained and is indicative of a trusted entity-assigned translation quality of the translated string. A relationship between a subset of the extracted features and the trusted entity-assigned translation score is identified.
摘要翻译： 训练机器翻译系统以产生指示翻译结果的质量的置信度分数。 使用机器翻译器翻译源字符串以生成目标字符串。 从机器翻译器提取表示所执行的翻译操作的特征。 获得受信任的实体分配的翻译分数，并且指示被翻译的字符串的受信任的实体分配的翻译质量。 识别提取的特征的子集与可信实体分配的翻译分数之间的关系。
-
公开(公告)号：US20120179450A1
公开(公告)日：2012-07-12
申请号：US13409419
申请日：2012-03-01
IPC分类号： G06F17/28
CPC分类号： G06F17/289
摘要： A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.
摘要翻译： 一种翻译方法包括将源文本部分上传到后端处理器。 后端处理器识别与源文本部分相关联的翻译知识的子集。 后端处理器将子集下载到前端处理器。 翻译引擎在前端处理器上运行。 翻译引擎生成作为子集的函数的源文本部分的翻译。
-
公开(公告)号：US08082143B2
公开(公告)日：2011-12-20
申请号：US12499379
申请日：2009-07-08
IPC分类号： G06F17/28
CPC分类号： G06F17/2818 , G06F17/28
摘要： In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.
摘要翻译： 在本发明的一个实施例中，解码器接收依赖树作为源语言输入，并访问产生在对数线性框架中组合的输出的一组统计模型。 解码器还访问树形图转换对的表，并基于对依赖树的转换对的访问，并且基于统计模型的应用，基于源依赖关系树返回目标依赖关系树。
-
公开(公告)号：US20090326911A1
公开(公告)日：2009-12-31
申请号：US12146531
申请日：2008-06-26
IPC分类号： G06F17/28
CPC分类号： G06F17/2872 , G06F17/2827
摘要： Many machine translation scenarios involve the generation of a language translation rule set based on parallel training corpuses (e.g., sentences in a first language and word-for-word translations into a second language.) However, the translation of a source corpus in a source language to a target corpus in a target language involves at least two aspects: selecting elements of the target language to match the elements of the source corpus, and ordering the target elements according to the semantic organization of the source corpus and the grammatic rules of the target language. The breadth of generalization of the translation rules derived from the training may be improved, while retaining contextual information, by formulating language order templates that specify orderings of small sets of target elements according to target element types. These language order templates may be represented with varying degrees of association with the alignment rules derived from the training in order to improve the scope of target elements to which the ordering rules and alignment rules may be applied.
摘要翻译： 许多机器翻译方案涉及到基于平行训练语料库（例如，第一语言中的句子和逐字翻译成第二语言）的语言翻译规则集的生成。然而，源语料库在源中的翻译 目标语言中的目标语料库的语言至少涉及两个方面：选择目标语言的元素以匹配源语料库的元素，并根据源语料库的语义组织和语法规则对目标元素进行排序 目标语言。 可以通过根据目标元素类型制定指定小组目标元素的排序的语言顺序模板，同时保留上下文信息，从而改进从训练中导出的翻译规则的广义化。 这些语言顺序模板可以以与从训练导出的对准规则的不同程度的关联来表示，以便改进可以应用排序规则和对准规则的目标元素的范围。
-
公开(公告)号：US20090271177A1
公开(公告)日：2009-10-29
申请号：US12499379
申请日：2009-07-08
IPC分类号： G06F17/28
CPC分类号： G06F17/2818 , G06F17/28
摘要： In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.
摘要翻译： 在本发明的一个实施例中，解码器接收依赖树作为源语言输入，并访问产生在对数线性框架中组合的输出的一组统计模型。 解码器还访问树形图转换对的表，并基于对依赖树的转换对的访问，并且基于统计模型的应用，基于源依赖关系树返回目标依赖关系树。
-
公开(公告)号：US07505894B2
公开(公告)日：2009-03-17
申请号：US11014152
申请日：2004-12-16
IPC分类号： G06F17/28
CPC分类号： G06F17/2818 , G06F17/28
摘要： In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.
摘要翻译： 在本发明的一个实施例中，解码器接收依赖树作为源语言输入，并访问产生在对数线性框架中组合的输出的一组统计模型。 解码器还访问树形图转换对的表，并基于对依赖树的转换对的访问，并且基于统计模型的应用，基于源依赖关系树返回目标依赖关系树。
-
-
-
-
-
-
-
-
-
搜索结果
20 条记录 (耗时 0.021 秒)
