Commit 12fe746f by xiaotong

bbl and bug fixes

parent 2ff6c1c7
......@@ -501,7 +501,7 @@ His house is on the south bank of the river.
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\parinterval从实践的角度,机器翻译的发展主要可以归功于两方面的推动作用:开源系统和评测。开源系统通过代码共享的方式使得最新的研究成果可以快速传播,同时实验结果可以复现。而评测比赛,使得机器翻译各个研究组织在同一平台进行较为合理的良性竞争对比,共同推动机器翻译的发展与进步。此外,开源项目也促进了不同团队之间的协作,让研究人员在同一个平台上集中力量攻关。
\subsubsection{SMT}\index{Chapter1.7.1.1}
\subsubsection{统计机器翻译开源系统}\index{Chapter1.7.1.1}
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\vspace{0.5em}
\begin{itemize}
......@@ -529,7 +529,7 @@ His house is on the south bank of the river.
\end{itemize}
\vspace{0.5em}
\subsubsection{NMT}\index{Chapter1.7.1.2}
\subsubsection{神经机器翻译开源系统}\index{Chapter1.7.1.2}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\vspace{0.5em}
\begin{itemize}
......@@ -549,7 +549,7 @@ His house is on the south bank of the river.
\vspace{0.5em}
\item THUMT:清华大学NLP团队实现的Transformer等模型、支持多GPU训练和解码、分布式训练。\url{https://github.com/THUNLP-MT/THUMT}
\vspace{0.5em}
\item NiuTrans.NMT:东北大学NLP团队基于NiuTensor工具集实现的Transformer 模型以及前馈网络语言模型。\url{https://g\-ithub.com/NiuTrans/NiuTensor}
\item NiuTrans.NMT:东北大学NLP团队基于NiuTensor工具集实现的Transformer 模型以及前馈网络语言模型。\url{https://github.com/NiuTrans/NiuTensor}
\vspace{0.5em}
\item MARIANNMT:主要由微软翻译团队搭建,使用纯C++实现的用于GPU/CPU\\训练和解码的引擎。\url{https://marian-nmt.github.io/}
\vspace{0.5em}
......@@ -573,17 +573,17 @@ His house is on the south bank of the river.
\vspace{0.5em}
\begin{itemize}
\item NIST机器翻译评测开始于2001年,由美国国家标准技术研究所主办,作为美国国防高级计划署(DARPA)中TIDES计划的重要组成部分,为机器翻译的技术对比以及沟通交流提供了良好的平台。其宗旨在于吸引更多研究人员关注到机器翻译技术的核心问题,为大家提供良好的参与平台。NIST评测主要评价阿拉伯语和汉语到英语的翻译效果,评价方法一般采用人工评价与自动评价相结合的方式,在NIST 2015中,人工评价指标包括``完全可用'',``少量修改后可用'',``句义可懂但缺乏细节'',``不可读或与原文句义不相关'',``句义存在误导'',分别为3,2,1,0,-1分。自动评价也使用多种方式,包括BLEU,METEOR,TER以及HyTER。此外NIST从2016 年起开始对稀缺语言资源技术进行评估,其中机器翻译作为其重要组成部分共同参与评测,评测指标主要为BLEU。除对机器翻译系统进行评测之外,NIST在2008 和2010年对于机器翻译的自动评价方法(MetricsMaTr)也进行了评估,以鼓励更多研究人员对现有评价方法进行改进或提出更加贴合人工评价的方法。同时NIST评测所提供的数据集由于其认可度、数据质量较高等特点受到众多科研人员喜爱,如MT04,MT06等平行语料经常被科研人员在实验中使用。更多NIST的机器翻译评测相关信息可参考官网:\url{https://www.nist.gov/programs-projects/machine-translation}
\item NIST机器翻译评测开始于2001年,由美国国家标准技术研究所主办,作为美国国防高级计划署(DARPA)中TIDES计划的重要组成部分,为机器翻译的技术对比以及沟通交流提供了良好的平台。其宗旨在于吸引更多研究人员关注到机器翻译技术的核心问题,为大家提供良好的参与平台。早期,NIST评测主要评价阿拉伯语和汉语等语言到英语的翻译效果,评价方法一般采用人工评价与自动评价相结合的方式。人工评价采用5分制评价。自动评价使用多种方式,包括BLEU,METEOR,TER以及HyTER。此外NIST从2016 年起开始对稀缺语言资源技术进行评估,其中机器翻译作为其重要组成部分共同参与评测,评测指标主要为BLEU。除对机器翻译系统进行评测之外,NIST在2008 和2010年对于机器翻译的自动评价方法(MetricsMaTr)也进行了评估,以鼓励更多研究人员对现有评价方法进行改进或提出更加贴合人工评价的方法。同时NIST评测所提供的数据集由于其认可度、数据质量较高等特点受到众多科研人员喜爱,如MT04,MT06等(汉英)平行语料经常被科研人员在实验中使用。更多NIST的机器翻译评测相关信息可参考官网:\url{https://www.nist.gov/programs-projects/machine-translation}
\vspace{0.5em}
\item CWMT是国内机器翻译领域顶级研讨会,兴起于2005年,至今已连续成功召开了12届,共组织6次机器翻译评测、1次开源系统模块开发以及两次战略研讨,对国内机器翻译相关技术的发展产生了深远影响。该评测主要针对汉语、英语以及国内的少数民族语言(蒙古语、藏语、维吾尔语等)进行评测,领域包括新闻、口语、政府文件等,不同语言方向对应的领域也有所不同。评价方式不同届略有不同,主要采用自动评价的方式,CWMT 2013则针对某些领域增设人工评价。自动评价的指标一般包括BLEU-SBP、BLEU-NIST、TER、METEOR、NIST、GTM、mWER、mPER以及ICT等,其中以\\BLEU-SBP为主,汉语为目标语的翻译采用基于字符的评价方式,面向英语的翻译基于词进行评价。每年该评测吸引国内外近15-20家企业及科研机构参赛,包括日本NICT-ATR研究所、微软亚洲研究院、韩国SYSTRAN公司等,业内认可度颇高,19年更名为CCMT。更多CWMT 的机器翻译评测相关信息可参考官网:\url{http://www.ai-ia.ac.cn/cwmt2015/evaluation.html} (链接为CWMT 2015)。
\item CCMT(全国机器翻译大会),前身为CWMT(全国机器翻译研讨会)是国内机器翻译领域顶级会议,兴起于2005年,并组织多次机器翻译评测,对国内机器翻译相关技术的发展产生了深远影响。该评测主要针对汉语、英语以及国内的少数民族语言(蒙古语、藏语、维吾尔语等)进行评测,领域包括新闻、口语、政府文件等,不同语言方向对应的领域也有所不同。评价方式不同届略有不同,主要采用自动评价的方式,自CWMT 2013起则针对某些领域增设人工评价。自动评价的指标一般包括BLEU-SBP、BLEU-NIST、TER、METEOR、NIST、GTM、mWER、mPER 以及ICT 等,其中以\\BLEU-SBP 为主,汉语为目标语的翻译采用基于字符的评价方式,面向英语的翻译基于词进行评价。每年该评测吸引国内外近数十家家企业及科研机构参赛,业内认可度颇高,19年更名为CCMT。更多CWMT 的机器翻译评测相关信息可参考官网:\url{http://www.ai-ia.ac.cn/cwmt2015/evaluation.html} (链接为CWMT 2015)。
\vspace{0.5em}
\item WMT由Special Interest Group for Machine Translation(SIGMT)主办,自2006年起每年一次,已累计举办了11 届,是一个针对机器翻译多种任务的综合性会议,包括多领域翻译评测任务、评价任务(如自动评价标准评测、翻译质量评估评测等)以及其他技术相关任务(如文档对齐评测等)。其翻译评测任务中其涉及的语言范围较广,包括英语、德语、芬兰语、捷克语、罗马尼亚语等十多种语言,翻译方向一般以英语为核心,探索英语与其他欧洲语言翻译的性能,领域包括新闻、信息技术、生物医学。WMT在在评价方面类似其他评测,也采用人工评价与自动评价相结合的方式,自动评价的指标一般为NIST、BLEU以及TER 等。此外WMT公开的评测数据集也经常被研究欧洲语系的机器翻译相关人员所使用。更多WMT 的机器翻译评测相关信息可参考官网:\url{http://www.sigmt.org/}
\item WMT由Special Interest Group for Machine Translation(SIGMT)主办,自2006年起每年一次,是一个针对机器翻译多种任务的综合性会议,包括多领域翻译评测任务、评价任务(如自动评价标准评测、翻译质量评估评测等)以及其他技术相关任务(如文档对齐评测等)。现在WMT任务已经成为机器翻译领域的旗舰评测任务,很多研究工作都已WMT任务作为基准。其翻译评测任务中其涉及的语言范围较广,包括英语、德语、芬兰语、捷克语、罗马尼亚语等十多种语言,翻译方向一般以英语为核心,探索英语与其他欧洲语言翻译的性能,领域包括新闻、信息技术、生物医学。WMT在在评价方面类似其他评测,也采用人工评价与自动评价相结合的方式,自动评价的指标一般为NIST、BLEU以及TER 等。此外WMT公开的评测数据集也经常被研究欧洲语系的机器翻译相关人员所使用。更多WMT 的机器翻译评测相关信息可参考官网:\url{http://www.sigmt.org/}
\vspace{0.5em}
\item 从2004年开始举办的IWSLT评测逐渐在国际舞台备受瞩目,它主要关注口语相关的机器翻译任务,使用材料主要包括TED talks的多语言字幕以及QED 教育讲座影片字幕等,语言涉及英语、法语、德语、捷克语、汉语、阿拉伯语等众多语言。此外在IWSLT 2016 中还加入了对于日常对话的翻译评测,尝试将微软Skype中一种语言的对话翻译成其他语种。评价方式一般采用自动评价的模式,评价标准和WMT类似,一般为NIST、BLEU以及TER。另外,IWSLT除了对文本到文本的翻译评测外,还有自动语音识别以及语音转另一种语言的文本的评测。更多IWSLT的机器翻译评测相关信息可参考官网:\url{https://workshop2016.iwslt.org/} (链接为IWSLT2016)
\vspace{0.5em}
\item NTCIR计划是由日本国家科学咨询系统中心策划主办的,旨在建立一个用在自然语言处理以及信息检索相关任务上的日文标准测试集。从1999年至今,NTCIR评测任务已举办13届,每届可能涉及不同的评测任务。在NTCIR-9的和NTCIR-10中开设的Patent Machine Translation(PatentMT)任务主要针对专利领域进行翻译测试,其目的在于促进机器翻译在专利领域的发展和应用。在两届PatentMT中评测的语言方向包括中到英、日到英、英到日,中到英提供100万专利描述平行句对,日英互译提供300万平行句对,参与者可选择某个或某些语言方向参与评测。在NTCIR-9中,评测方式采取人工评价与自动评价相结合,以人工评价为主导。人工评价主要根据忠实度和流畅度进行评估,自动评价采用BLEU、NIST的方式进行。NTCIR-10评价方式在此基础上增加了专利审查评估、时间评估以及多语种评估,分别考察机器翻译系统在专利领域翻译的实用性、耗时情况以及不同语种的翻译效果等。更多NTCIR评测相关信息可参考官网:\url{http://research.nii.ac.jp/ntcir/index-en.html}
\vspace{0.5em}
\item 日本举办的机器翻译评测WAT是最近几年开始的,至今已成功举办三届,由日本科学振兴机构(JST)、情报通信研究机构(NICT)等多家机构共同组织,旨在为亚洲各国之间交流融合提供便宜之处。语言方向主要包括亚洲主流语言(汉语、韩语以及印地语等)以及英语对日语的翻译,领域丰富多样,包括学术论文、专利、新闻、食谱等。评价方式包括自动评价(BLEU、RIBES以及AMFM 等)以及人工评价,其特点在于对于测试语料以段落为单位进行评价,考察其上下文关联的翻译效果。更多WAT的机器翻译评测相关信息可参考官网:\url{http://lotus.kuee.kyoto-u.ac.jp/WAT/}
\item 日本举办的机器翻译评测WAT也是亚洲范围内的重要评测之一,由日本科学振兴机构(JST)、情报通信研究机构(NICT)等多家机构共同组织,旨在为亚洲各国之间交流融合提供便宜之处。语言方向主要包括亚洲主流语言(汉语、韩语以及印地语等)以及英语对日语的翻译,领域丰富多样,包括学术论文、专利、新闻、食谱等。评价方式包括自动评价(BLEU、RIBES以及AMFM 等)以及人工评价,其特点在于对于测试语料以段落为单位进行评价,考察其上下文关联的翻译效果。更多WAT的机器翻译评测相关信息可参考官网:\url{http://lotus.kuee.kyoto-u.ac.jp/WAT/}
\end{itemize}
\vspace{0.5em}
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