Commit dfc7b3c5 by xiaotong

updates

parent daddd6be
......@@ -598,7 +598,7 @@ His house is on the south bank of the river.
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\begin{itemize}
\item ACL,全称Annual Meeting of the Association for Computational Linguistics,是自然语言处理领域最高级别的会议。每年举办一次,主题包括机器翻译,对话,信息抽取,信息检索,语言生成,语言资源,多模态,情感分析,语音等多个方面
\item ACL,全称Annual Meeting of the Association for Computational Linguistics,是自然语言处理领域最高级别的会议。每年举办一次,主题涵盖计算语言学的所有方向
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\item NAACL,全称Annual Meeting of the Association for Computational Linguistics,为ACL北美分会,在自然语言处理领域也属于顶级会议,每年会选择一个北美城市召开会议。
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......@@ -616,14 +616,14 @@ His house is on the south bank of the river.
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\item CCL,全称Conference on Computational Linguistics,中文为中国计算语言学大会。中国计算语言学大会创办于1991年,由中国中文信息学会计算语言学专业委员会负责组织。经过20余年的发展,中国计算语言学大会已成为国内自然语言处理领域权威性最高、规模和影响最大的学术会议。作为中国中文信息学会(国内一级学会)的旗舰会议,CCL聚焦于中国境内各类语言的智能计算和信息处理,为研讨和传播计算语言学最新学术和技术成果提供了最广泛的高层次交流平台。
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\item NLPCC,全名The CCF International Conference on Natural Language Processing and Chinese Computing。NLPCC 是由中国计算机学会(CCF)主办的 CCF 中文信息技术专业委员会年度学术会议,同时也是CCF推荐C类会议,专注于自然语言处理及中文计算领域的学术和应用创新。会议自 2012 年开始举办,主要活动有主题演讲、论文报告、技术测评等多种形式。
\item NLPCC,全称The CCF International Conference on Natural Language Processing and Chinese Computing。NLPCC 是由中国计算机学会(CCF)主办的 CCF 中文信息技术专业委员会年度学术会议,专注于自然语言处理及中文计算领域的研究和应用创新。会议自 2012 年开始举办,主要活动有主题演讲、论文报告、技术测评等多种形式。
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\item CCMT,全The China Conference on Machine Translation,中国机器翻译研讨会,由中国中文信息学会主办,旨在为国内外机器翻译界同行提供一个平台,加强国内外同行的学术交流,促进中国机器翻译事业。CCMT不仅是国内机器翻译领域最具影响力、最权威的学术和评测活动,而且也代表着汉语与民族语言翻译技术的最高水准,对民族语言技术发展具有重要意义。
\item CCMT,全The China Conference on Machine Translation,中国机器翻译研讨会,由中国中文信息学会主办,旨在为国内外机器翻译界同行提供一个平台,加强国内外同行的学术交流,促进中国机器翻译事业。CCMT不仅是国内机器翻译领域最具影响力、最权威的学术和评测活动,而且也代表着汉语与民族语言翻译技术的最高水准,对民族语言技术发展具有重要意义。
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\end{itemize}
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除了会议之外,《Computational Linguistics》、《Machine Translation》、《Transactions of the Association for Computational Linguistics》等期刊也发表了许多与机器翻译相关的重要论文。
除了会议之外,《Computational Linguistics》、《Machine Translation》、《Transactions of the Association for Computational Linguistics》、《IEEE/ACM Transactions on Audio, Speech, and Language Processing》、《ACM Transactions on Asian and Low Resource Language Information Processing》、《Natural Language Engineering》等期刊也发表了许多与机器翻译相关的重要论文。
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\contentsline {subsubsection}{(二)优化}{107}{section*.98}
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\contentsline {subsubsection}{词对齐模型}{121}{section*.112}
\contentsline {subsubsection}{词对齐模型}{121}{section*.112}%
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\contentsline {subsection}{\numberline {3.6.2}Deficiency}{124}{subsection.3.6.2}
\contentsline {subsection}{\numberline {3.6.2}Deficiency}{124}{subsection.3.6.2}%
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\contentsline {chapter}{\numberline {4}人工神经网络和神经语言建模}{129}{chapter.4}
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\contentsline {section}{\numberline {4.1}深度学习与人工神经网络}{130}{section.4.1}
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\contentsline {subsubsection}{(一)早期的人工神经网络和第一次寒冬}{130}{section*.114}
\contentsline {subsubsection}{(一)早期的人工神经网络和第一次寒冬}{130}{section*.114}%
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\contentsline {subsubsection}{(二)神经网络的第二次高潮和第二次寒冬}{131}{section*.115}
\contentsline {subsubsection}{(二)神经网络的第二次高潮和第二次寒冬}{131}{section*.115}%
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\contentsline {subsubsection}{(三)深度学习和神经网络的崛起}{132}{section*.116}
\contentsline {subsubsection}{(三)深度学习和神经网络的崛起}{132}{section*.116}%
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\contentsline {subsection}{\numberline {4.1.2}为什么需要深度学习}{133}{subsection.4.1.2}
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\contentsline {subsubsection}{(一)端到端学习和表示学习}{133}{section*.118}
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\contentsline {subsubsection}{(二)深度学习的效果}{134}{section*.120}
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\contentsline {subsubsection}{(一)标量、向量和矩阵}{135}{section*.122}
\contentsline {subsubsection}{(一)标量、向量和矩阵}{135}{section*.122}%
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\contentsline {subsubsection}{(二)矩阵的转置}{136}{section*.123}
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\contentsline {subsubsection}{(三)矩阵加法和数乘}{136}{section*.124}
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\contentsline {subsubsection}{(四)矩阵乘法和矩阵点乘}{137}{section*.125}
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\contentsline {subsubsection}{(五)线性映射}{138}{section*.126}
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\contentsline {subsubsection}{(六)范数}{139}{section*.127}
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\contentsline {subsubsection}{(一)感知机\ \raisebox {0.5mm}{------}\ 最简单的人工神经元模型}{141}{section*.130}
\contentsline {subsubsection}{(一)感知机\ \raisebox {0.5mm}{------}\ 最简单的人工神经元模型}{141}{section*.130}%
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\contentsline {subsubsection}{(二)神经元内部权重}{141}{section*.133}
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\contentsline {subsubsection}{(三)神经元的输入\ \raisebox {0.5mm}{------}\ 离散 vs 连续}{142}{section*.135}
\contentsline {subsubsection}{(三)神经元的输入\ \raisebox {0.5mm}{------}\ 离散 vs 连续}{142}{section*.135}%
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\contentsline {subsubsection}{(四)神经元内部的参数学习}{143}{section*.137}
\contentsline {subsubsection}{(四)神经元内部的参数学习}{143}{section*.137}%
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\contentsline {subsection}{\numberline {4.2.3}多层神经网络}{144}{subsection.4.2.3}
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\contentsline {subsubsection}{(一)线性变换和激活函数}{144}{section*.139}
\contentsline {subsubsection}{(一)线性变换和激活函数}{144}{section*.139}%
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\contentsline {subsubsection}{(二)单层神经网络$\rightarrow $多层神经网络}{146}{section*.146}
\contentsline {subsubsection}{(二)单层神经网络$\rightarrow $多层神经网络}{146}{section*.146}%
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\contentsline {subsection}{\numberline {4.2.4}函数拟合能力}{148}{subsection.4.2.4}
\contentsline {subsection}{\numberline {4.2.4}函数拟合能力}{148}{subsection.4.2.4}%
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\contentsline {section}{\numberline {4.3}神经网络的张量实现}{151}{section.4.3}
\contentsline {section}{\numberline {4.3}神经网络的张量实现}{151}{section.4.3}%
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\contentsline {subsection}{\numberline {4.3.1} 张量及其计算}{151}{subsection.4.3.1}
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\contentsline {subsubsection}{(一)张量}{151}{section*.156}
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\contentsline {subsubsection}{(二)张量的矩阵乘法}{153}{section*.159}
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\contentsline {subsubsection}{(三)张量的单元操作}{154}{section*.161}
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\contentsline {subsection}{\numberline {4.3.2}张量的物理存储形式}{155}{subsection.4.3.2}
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\contentsline {subsection}{\numberline {4.3.3}使用开源框架实现张量计算}{156}{subsection.4.3.3}
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\contentsline {subsection}{\numberline {4.3.4}神经网络中的前向传播}{160}{subsection.4.3.4}
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\contentsline {subsection}{\numberline {4.3.5}神经网络实例}{161}{subsection.4.3.5}
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\contentsline {section}{\numberline {4.4}神经网络的参数训练}{162}{section.4.4}
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\contentsline {subsection}{\numberline {4.4.1}损失函数}{163}{subsection.4.4.1}
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\contentsline {subsection}{\numberline {4.4.2}基于梯度的参数优化}{164}{subsection.4.4.2}
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\contentsline {subsubsection}{(二)梯度获取}{167}{section*.181}
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\contentsline {subsubsection}{(三)基于梯度的方法的变种和改进}{169}{section*.185}
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\contentsline {subsection}{\numberline {4.4.3}参数更新的并行化策略}{172}{subsection.4.4.3}
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\contentsline {subsubsection}{(二)梯度爆炸现象及解决方法}{175}{section*.192}
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\contentsline {subsubsection}{(二)基于循环神经网络的语言模型}{187}{section*.209}
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\contentsline {subsubsection}{(三)基于自注意力机制的语言模型}{188}{section*.211}
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\contentsline {subsubsection}{(二)ELMO模型}{194}{section*.223}
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\contentsline {section}{\numberline {5.3}基于循环神经网络的翻译模型及注意力机制}{212}{section.5.3}
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......@@ -58,7 +58,7 @@
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%{\Large 副标题是否需要}\\[20pt] % Subtitle
{\huge 肖桐}}}; % Author name
{\huge 肖桐\ \ 朱靖波}}}; % Author name
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......@@ -71,15 +71,15 @@
~\vfill
\thispagestyle{empty}
\noindent Copyright \copyright\ 2020 Xiao Tong\\ % Copyright notice
\noindent Copyright \copyright\ 2020 Xiao Tong and Jingbo Zhu\\ % Copyright notice
\noindent \textsc{Published by \red{Publisher}}\\ % Publisher
\noindent \textsc{Published by \red{NLP Lab. at Northeastern University\ /\ NiuTrans Research}}\\ % Publisher
\noindent \textsc{\url{http://47.105.50.196/NiuTrans/Toy-MT-Introduction/tree/master/Book}}\\ % URL
\noindent {\red{Licensed under the Creative Commons Attribution-NonCommercial 3.0 Unported License (the ``License''). You may not use this file except in compliance with the License. You may obtain a copy of the License at \url{http://creativecommons.org/licenses/by-nc/3.0}. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \textsc{``as is'' basis, without warranties or conditions of any kind}, either express or implied. See the License for the specific language governing permissions and limitations under the License.}}\\ % License information, replace this with your own license (if any)
\noindent \textit{First printing, \red{March 2019}} % Printing/edition date
\noindent \textit{First printing, \red{March 2020}} % Printing/edition date
%----------------------------------------------------------------------------------------
......
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