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Book/Chapter2/Figures/figure-analysis-of-sentence-participle&syntactic.tex
......@@ -6,7 +6,7 @@
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Book/Chapter7/Chapter7.tex
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Book/Chapter7/Figures/Post-Norm-vs-Pre-Norm.tex
......@@ -6,13 +6,13 @@
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Book/Chapter7/Figures/dynamic-linear-aggregation-network-structure.tex
......@@ -4,21 +4,21 @@
\node [anchor=north,rectangle, inner sep=0mm,minimum height=1.2em,minimum width=2em,rounded corners=5pt,thick] (n1) at (0, 0) {编码端};
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......
Book/Chapter7/Figures/progressive-training.tex
......@@ -22,10 +22,10 @@
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......
Book/ChapterPreface/ChapterPreface.tex 0 → 100644
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Book/Chapters/Biblio-Database.bib deleted 100644 → 0
%% Saved with string encoding Western (ASCII)
@article{Aho-Ullman:1969:JCSS,
author = {Aho, Alfred V. and Ullman, Jeffrey D.},
title = {Syntax directed translations and the pushdown assembler},
journal = {Journal of Computer and System Sciences},
pages = {37--57},
volume = {3},
year = {1969}
}
@article{alshawi-EtAl:2000:CL,
author = {Alshawi, Hiyan and Bangalore, Srinivas and Douglas, Shona},
title = {Learning dependency translation models as collections of finite state head transducers},
journal = {Computational Linguistics},
mumber = {1},
pages = {45--60},
volume = {26},
year = {2000}
}
@article{brown-EtAl:1993:CL,
author = {Brown, Peter E. and Pietra, Stephen A. Della and Pietra, Vincent J. Della and Mercer, Robert L.},
title = {The Mathematics of Statistical Machine Translation: Parameter Estimation },
journal = {Computational Linguistics},
mumber = {2},
pages = {263--311},
volume = {19},
year = {1993}
}
@article{bergert-EtAl:1996:CL,
author = {Bergert, Adam L. and Pietra, Vincent J. Della and Pietra, Stephen A. Della},
title = {A maximum entropy approach to natural language processing},
journal = {Computational Linguistics},
mumber = {1},
pages = {39--71},
volume = {22},
year = {1996}
}
@InProceedings{cer-EtAl:2010:DEMO,
author = {Cer, Daniel and Galley, Michel and Jurafsky, Daniel and Manning, Christopher D.},
title = {Phrasal: A Statistical Machine Translation Toolkit for Exploring New Model Features},
booktitle = {Proceedings of the NAACL HLT 2010 Demonstration Session},
month = {June},
year = {2010},
address = {Los Angeles, California},
publisher = {Association for Computational Linguistics},
pages = {9--12},
url = {http://www.aclweb.org/anthology/N10-2003}
}
@InProceedings{chiang:2005:ACL,
author = {Chiang, David},
title = {A Hierarchical Phrase-Based Model for Statistical Machine Translation},
booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)},
month = {June},
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
pages = {263--270},
url = {http://www.aclweb.org/anthology/P05-1033},
doi = {10.3115/1219840.1219873}
}
@InProceedings{chiang-kevin:2006:ACL,
author = {Chiang, David and Knight, Kevin},
title = {An introduction to synchronous grammars},
booktitle = {Proceedings of the 44rd Annual Meeting of the Association for Computational Linguistics (ACL'05)},
year = {2006},
publisher = {Association for Computational Linguistics}
}
@article{chiang:2007:CL,
author = {David Chiang},
title = {Hierarchical Phrase-Based Translation},
journal = {Computational Linguistics},
mumber = {2},
pages = {45--60},
volume = {33},
year = {2007}
}
@InProceedings{chiang-EtAl:2008:EMNLP,
author = {Chiang, David and DeNeefe, Steve and Chan, Yee Seng and Ng, Hwee Tou},
title = {Decomposability of Translation Metrics for Improved Evaluation and Efficient Algorithms},
booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing},
month = {October},
year = {2008},
address = {Honolulu, Hawaii},
publisher = {Association for Computational Linguistics},
pages = {610--619},
url = {http://www.aclweb.org/anthology/D08-1064}
}
@InProceedings{denero-EtAl:2010:NAACLHLT,
author = {DeNero, John and Kumar, Shankar and Chelba, Ciprian and Och, Franz},
title = {Model Combination for Machine Translation},
booktitle = {Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
month = {June},
year = {2010},
address = {Los Angeles, California},
publisher = {Association for Computational Linguistics},
pages = {975--983},
url = {http://www.aclweb.org/anthology/N10-1141}
}
@InProceedings{dyer-EtAl:2010:Demos,
author = {Dyer, Chris and Lopez, Adam and Ganitkevitch, Juri and Weese, Jonathan and Ture, Ferhan and Blunsom, Phil and Setiawan, Hendra and Eidelman, Vladimir and Resnik, Philip},
title = {cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models},
booktitle = {Proceedings of the ACL 2010 System Demonstrations},
month = {July},
year = {2010},
address = {Uppsala, Sweden},
publisher = {Association for Computational Linguistics},
pages = {7--12},
url = {http://www.aclweb.org/anthology/P10-4002}
}
@InProceedings{eisner:2003:ACL-companion,
author = {Jason Eisner},
title = {Learning Non-Isomorphic Tree Mappings for Machine Translation},
booktitle = {The Companion Volume to the Proceedings of 41st Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2003},
address = {Sapporo, Japan},
publisher = {Association for Computational Linguistics},
pages = {205--208},
url = {http://www.aclweb.org/anthology/P03-2039},
doi = {10.3115/1075178.1075217}
}
@InProceedings{galley-EtAl:2006:COLACL,
author = {Galley, Michel and Graehl, Jonathan and Knight, Kevin and Marcu, Daniel and DeNeefe, Steve and Wang, Wei and Thayer, Ignacio},
title = {Scalable Inference and Training of Context-Rich Syntactic Translation Models},
booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2006},
address = {Sydney, Australia},
publisher = {Association for Computational Linguistics},
pages = {961--968},
url = {http://www.aclweb.org/anthology/P06-1121},
doi = {10.3115/1220175.1220296}
}
@InProceedings{galley-manning:2008:EMNLP,
author = {Galley, Michel and Manning, Christopher D.},
title = {A Simple and Effective Hierarchical Phrase Reordering Model},
booktitle = {Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing},
month = {October},
year = {2008},
address = {Honolulu, Hawaii},
publisher = {Association for Computational Linguistics},
pages = {848--856},
url = {http://www.aclweb.org/anthology/D08-1089}
}
@InProceedings{huang-chiang:2005:IWPT,
author = {Huang, Liang and Chiang, David},
title = {Better k-best Parsing},
booktitle = {Proceedings of the Ninth International Workshop on Parsing Technology},
month = {October},
year = {2005},
address = {Vancouver, British Columbia},
publisher = {Association for Computational Linguistics},
pages = {53--64},
url = {http://www.aclweb.org/anthology/W/W05/W05-1506}
}
@inproceedings{koehn-EtAl:2003:NAACL,
author = {Koehn, Philipp and Och, Franz and Marcu, Daniel},
title = {Statistical Phrase-Based Translation},
booktitle = {Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics},
month = {June},
year = {2003},
address = {Edmonton}
publisher = {The North American Chapter of the Association for Computational Linguistics},
pages = {48--54},
url = {http://aclweb.org/anthology-new/N/N03/N03-1017.pdf}
}
@inproceedings{koehn-EtAl:2007:PosterDemo,
author = {Koehn, Philipp and Hoang, Hieu and Birch, Alexandra and Callison-Burch, Chris and Federico, Marcello and Bertoldi, Nicola and Cowan, Brooke and Shen, Wade and Moran, Christine and Zens, Richard and Dyer, Chris and Bojar, Ondrej and Constantin, Alexandra and Herbst, Evan},
title = {Moses: Open Source Toolkit for Statistical Machine Translation},
booktitle = {Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions},
month = {June},
year = {2007},
address = {Prague, Czech Republic},
publisher = {Association for Computational Linguistics},
pages = {177--180},
url = {http://www.aclweb.org/anthology/P07-2045}
}
@book{koehn:2010:smt,
author = {Koehn, Philipp},
title = {Statistical Machine Translation},
booktitle = {Statistical Machine Translation},
year = {2010},
publisher = {Cambridge University Press},
}
@InProceedings{li-EtAl:2009:WMT1,
author = {Li, Zhifei and Callison-Burch, Chris and Dyer, Chris and Khudanpur, Sanjeev and Schwartz, Lane and Thornton, Wren and Weese, Jonathan and Zaidan, Omar},
title = {{Joshua}: An Open Source Toolkit for Parsing-Based Machine Translation},
booktitle = {Proceedings of the Fourth Workshop on Statistical Machine Translation},
month = {March},
year = {2009},
address = {Athens, Greece},
publisher = {Association for Computational Linguistics},
pages = {135--139},
url = {http://www.aclweb.org/anthology/W09-0424}
}
@article{lopez:2008:ACMComputing,
author = {Lopez, Adam},
title = {Statistical Machine Translation},
journal = {ACM Computing Surveys},
mumber = {3},
pages = {1--49},
volume = {40},
year = {2008}
}
@InProceedings{mi-huang-liu:2008:ACLMain,
author = {Mi, Haitao and Huang, Liang and Liu, Qun},
title = {Forest-Based Translation},
booktitle = {Proceedings of ACL-08: HLT},
month = {June},
year = {2008},
address = {Columbus, Ohio},
publisher = {Association for Computational Linguistics},
pages = {192--199},
url = {http://www.aclweb.org/anthology/P/P08/P08-1023}
}
@InProceedings{marcu-wong:2002:EMNLP02,
author = {Marcu, Daniel and Wong, Daniel},
title = {A Phrase-Based,Joint Probability Model for Statistical Machine Translation},
booktitle = {Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing},
month = {July},
year = {2002},
publisher = {Association for Computational Linguistics},
pages = {133--139},
url = {http://www.aclweb.org/anthology/W02-1018},
doi = {10.3115/1118693.1118711}
}
@InProceedings{och-ney:2002:ACL,
author = {Och, Franz and Ney, Hermann},
title = {Discriminative Training and Maximum Entropy Models for Statistical Machine Translation},
booktitle = {Proceedings of 40th Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2002},
address = {Philadelphia, Pennsylvania, USA},
publisher = {Association for Computational Linguistics},
pages = {295--302},
url = {http://www.aclweb.org/anthology/P02-1038},
doi = {10.3115/1073083.1073133}
}
@InProceedings{och:2003:ACL,
author = {Och, Franz},
title = {Minimum Error Rate Training in Statistical Machine Translation},
booktitle = {Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2003},
address = {Sapporo, Japan},
publisher = {Association for Computational Linguistics},
pages = {160--167},
url = {http://www.aclweb.org/anthology/P03-1021},
doi = {10.3115/1075096.1075117}
}
@InProceedings{papineni-EtAl:2002:ACL,
author = {Papineni, Kishore and Roukos, Salim and Ward, Todd and Zhu, Wei-Jing},
title = {Bleu: a Method for Automatic Evaluation of Machine Translation},
booktitle = {Proceedings of 40th Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2002},
address = {Philadelphia, Pennsylvania, USA},
publisher = {Association for Computational Linguistics},
pages = {311--318},
url = {http://www.aclweb.org/anthology/P02-1040},
doi = {10.3115/1073083.1073135}
}
@inproceedings{tillman:2004:HLTNAACL,
author = {Tillman, Christoph},
title = {A Unigram Orientation Model for Statistical Machine Translation},
booktitle = {HLT-NAACL 2004: Short Papers},
editor = {Susan Dumais, Daniel Marcu and Salim Roukos},
year = 2004,
month = {May 2 - May 7},
address = {Boston, Massachusetts, USA},
publisher = {Association for Computational Linguistics},
pages = {101--104}
}
@InProceedings{vilar-EtAl:2010:WMT,
author = {Vilar, David and Stein, Daniel and Huck, Matthias and Ney, Hermann},
title = {Jane: Open Source Hierarchical Translation, Extended with Reordering and Lexicon Models},
booktitle = {Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR},
month = {July},
year = {2010},
address = {Uppsala, Sweden},
publisher = {Association for Computational Linguistics},
pages = {262--270},
url = {http://www.aclweb.org/anthology/W10-1738}
}
@article{wu:1997:CL,
author = {Wu, Dekai},
title = {Stochastic inversion transduction grammars and bilingual parsing of parallel corpora},
journal = {Computational Linguistics},
mumber = {3},
pages = {377--404},
volume = {23},
year = {1997}
}
@InProceedings{xiao-EtAl:2009:CWMT,
author = {Xiao, Tong and Chen, Rushan and Li, Tianning and Zhu, Muhua and Zhu, Jingbo and Wang, Huizhen and Ren, Feiliang},
title = {NEUTrans: a Phrase-Based SMT System for CWMT2009},
booktitle = {Proceedings of the 5th China Workshop on Machine Translation},
month = {Sep},
year = {2009},
address = {Nanjing, China},
publisher = {CWMT},
url = {http://www.icip.org.cn/cwmt2009/downloads/papers/6.pdf}
}
@InProceedings{xiao-EtAl:2011:CWMT,
author = {Xiao, Tong and Zhang, Hao and Li, Qiang and Lu, Qi and Zhu, Jingbo and Ren, Feiliang and Wang, Huizhen},
title = {The NiuTrans Machine Translation System for CWMT2011},
booktitle = {Proceedings of the 6th China Workshop on Machine Translation},
month = {August},
year = {2011},
address = {Xiamen, China},
publisher = {CWMT},
}
@InProceedings{xiao-EtAl:2011:NTCIR,
author = {Xiao, Tong and Li, Qiang and Lu, Qi and Zhang, Hao and Ding, Haibo and Yao, Shujie and Xu, Xiaoming and Fei, Xiaoxu and Zhu, Jingbo and Ren, Feiliang and Wang, Huizhen},
title = {The NiuTrans Machine Translation System for NTCIR-9 PatentMT},
booktitle = {Proceedings of the NTCIR-9 Workshop Meeting},
month = {Dec},
year = {2011},
address = {Tokyo, Japan},
publisher = {NTCIR},
pages = {593--599},
url = {http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings9/NTCIR/04-NTCIR9-PATENTMT-XiaoT.pdf}
}
@InProceedings{xiong-liu-lin:2006:COLACL,
author = {Xiong, Deyi and Liu, Qun and Lin, Shouxun},
title = {Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation},
booktitle = {Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics},
month = {July},
year = {2006},
address = {Sydney, Australia},
publisher = {Association for Computational Linguistics},
pages = {521--528},
url = {http://www.aclweb.org/anthology/P06-1066},
doi = {10.3115/1220175.1220241}
}
@InProceedings{zhang-EtAl:2006:HLT-NAACL06-Main,
author = {Zhang, Hao and Huang, Liang and Gildea, Daniel and Knight, Kevin},
title = {Synchronous Binarization for Machine Translation},
booktitle = {Proceedings of the Human Language Technology Conference of the NAACL, Main Conference},
month = {June},
year = {2006},
address = {New York City, USA},
publisher = {Association for Computational Linguistics},
pages = {256--263},
url = {http://www.aclweb.org/anthology/N/N06/N06-1033}
}
@InProceedings{zollmann-venugopal:2006:WMT,
author = {Zollmann, Andreas and Venugopal, Ashish},
title = {Syntax Augmented Machine Translation via Chart Parsing},
booktitle = {Proceedings on the Workshop on Statistical Machine Translation},
month = {June},
year = {2006},
address = {New York City},
publisher = {Association for Computational Linguistics},
pages = {138--141},
url = {http://www.aclweb.org/anthology/W/W06/W06-3119}
}
Book/Chapters/bibliography.tex deleted 100644 → 0
%\bibliographystyle{plainnat}
%\bibliographystyle{FG-bibstyle}
%\addcontentsline{toc}{chapter}{Bibliography}
Book/Chapters/chapter1.tex deleted 100644 → 0
%----------------------------------------------------------------------------------------
% PART
%----------------------------------------------------------------------------------------
\part{哈哈哈哈哈～～爽朗的笑声}
%----------------------------------------------------------------------------------------
% CHAPTER 1
%----------------------------------------------------------------------------------------
\chapterimage{chapter_head_2.pdf} % Chapter heading image
\chapter{我是姜英俊}
\section{我姓姜，我很英俊}\index{Paragraphs of Text}
\lipsum[1-7] % Dummy text
%------------------------------------------------
\section{再说一遍？}\index{Citation}
This statement requires citation \cite{article_key}; this one is more specific \cite[162]{book_key}.
%------------------------------------------------
\section{咋的？}\index{Lists}
Lists are useful to present information in a concise and/or ordered way\footnote{Footnote example...}.
\subsection{我问你，你敢再说一遍不？}\index{Lists!Numbered List}
\begin{enumerate}
\item The first item
\item The second item
\item The third item
\end{enumerate}
\subsection{咋的？}\index{Lists!Bullet Points}
\begin{itemize}
\item The first item
\item The second item
\item The third item
\end{itemize}
\subsection{我靠，你还问我，我问你呢}\index{Lists!Descriptions and Definitions}
\begin{description}
\item[Name] Description
\item[Word] Definition
\item[Comment] Elaboration
\end{description}
\ No newline at end of file
Book/Chapters/chapter2.tex deleted 100644 → 0
%----------------------------------------------------------------------------------------
% CHAPTER 2
%----------------------------------------------------------------------------------------
\chapterimage{chapter_head_2.pdf} % Chapter heading image
\chapter{In-text Elements}
\section{Theorems}\index{Theorems}
This is an example of theorems.
\subsection{Several equations}\index{Theorems!Several Equations}
This is a theorem consisting of several equations.
\begin{theorem}[Name of the theorem]
In $E=\mathbb{R}^n$ all norms are equivalent. It has the properties:
\begin{align}
& \big| ||\mathbf{x}|| - ||\mathbf{y}|| \big|\leq || \mathbf{x}- \mathbf{y}||\\
& ||\sum_{i=1}^n\mathbf{x}_i||\leq \sum_{i=1}^n||\mathbf{x}_i||\quad\text{where $n$ is a finite integer}
\end{align}
\end{theorem}
\subsection{Single Line}\index{Theorems!Single Line}
This is a theorem consisting of just one line.
\begin{theorem}
A set $\mathcal{D}(G)$ in dense in $L^2(G)$, $|\cdot|_0$.
\end{theorem}
%------------------------------------------------
\section{Definitions}\index{Definitions}
This is an example of a definition. A definition could be mathematical or it could define a concept.
\begin{definition}[Definition name]
Given a vector space $E$, a norm on $E$ is an application, denoted $||\cdot||$, $E$ in $\mathbb{R}^+=[0,+\infty[$ such that:
\begin{align}
& ||\mathbf{x}||=0\ \Rightarrow\ \mathbf{x}=\mathbf{0}\\
& ||\lambda \mathbf{x}||=|\lambda|\cdot ||\mathbf{x}||\\
& ||\mathbf{x}+\mathbf{y}||\leq ||\mathbf{x}||+||\mathbf{y}||
\end{align}
\end{definition}
%------------------------------------------------
\section{Notations}\index{Notations}
\begin{notation}
Given an open subset $G$ of $\mathbb{R}^n$, the set of functions $\varphi$ are:
\begin{enumerate}
\item Bounded support $G$;
\item Infinitely differentiable;
\end{enumerate}
a vector space is denoted by $\mathcal{D}(G)$.
\end{notation}
%------------------------------------------------
\section{Remarks}\index{Remarks}
This is an example of a remark.
\begin{remark}
The concepts presented here are now in conventional employment in mathematics. Vector spaces are taken over the field $\mathbb{K}=\mathbb{R}$, however, established properties are easily extended to $\mathbb{K}=\mathbb{C}$.
\end{remark}
%------------------------------------------------
\section{Corollaries}\index{Corollaries}
This is an example of a corollary.
\begin{corollary}[Corollary name]
The concepts presented here are now in conventional employment in mathematics. Vector spaces are taken over the field $\mathbb{K}=\mathbb{R}$, however, established properties are easily extended to $\mathbb{K}=\mathbb{C}$.
\end{corollary}
%------------------------------------------------
\section{Propositions}\index{Propositions}
This is an example of propositions.
\subsection{Several equations}\index{Propositions!Several Equations}
\begin{proposition}[Proposition name]
It has the properties:
\begin{align}
& \big| ||\mathbf{x}|| - ||\mathbf{y}|| \big|\leq || \mathbf{x}- \mathbf{y}||\\
& ||\sum_{i=1}^n\mathbf{x}_i||\leq \sum_{i=1}^n||\mathbf{x}_i||\quad\text{where $n$ is a finite integer}
\end{align}
\end{proposition}
\subsection{Single Line}\index{Propositions!Single Line}
\begin{proposition}
Let $f,g\in L^2(G)$; if $\forall \varphi\in\mathcal{D}(G)$, $(f,\varphi)_0=(g,\varphi)_0$ then $f = g$.
\end{proposition}
%------------------------------------------------
\section{Examples}\index{Examples}
This is an example of examples.
\subsection{Equation and Text}\index{Examples!Equation and Text}
\begin{example}
Let $G=\{x\in\mathbb{R}^2:|x|<3\}$ and denoted by: $x^0=(1,1)$; consider the function:
\begin{equation}
f(x)=\left\{\begin{aligned} & \mathrm{e}^{|x|} & & \text{si $|x-x^0|\leq 1/2$}\\
& 0 & & \text{si $|x-x^0|> 1/2$}\end{aligned}\right.
\end{equation}
The function $f$ has bounded support, we can take $A=\{x\in\mathbb{R}^2:|x-x^0|\leq 1/2+\epsilon\}$ for all $\epsilon\in\intoo{0}{5/2-\sqrt{2}}$.
\end{example}
\subsection{Paragraph of Text}\index{Examples!Paragraph of Text}
\begin{example}[Example name]
\lipsum[2]
\end{example}
%------------------------------------------------
\section{Exercises}\index{Exercises}
This is an example of an exercise.
\begin{exercise}
This is a good place to ask a question to test learning progress or further cement ideas into students' minds.
\end{exercise}
%------------------------------------------------
\section{Problems}\index{Problems}
\begin{problem}
What is the average airspeed velocity of an unladen swallow?
\end{problem}
%------------------------------------------------
\section{Vocabulary}\index{Vocabulary}
Define a word to improve a students' vocabulary.
\begin{vocabulary}[Word]
Definition of word.
\end{vocabulary}
%----------------------------------------------------------------------------------------
% PART
%----------------------------------------------------------------------------------------
\part{Part Two}
\ No newline at end of file
Book/Chapters/chapter3.tex deleted 100644 → 0
%----------------------------------------------------------------------------------------
% CHAPTER 3
%----------------------------------------------------------------------------------------
\renewcommand\figurename{图}%将figure改为图
\renewcommand\tablename{表}%将figure改为图
\chapterimage{chapter_head_1.pdf} % Chapter heading image
\chapter{基于词的翻译模型}
\hspace{2em}统计机器翻译和神经机器翻译在当前具有统治性意义。这两种方法各有优缺点，并没有那种方法具有绝对的优势。但从研究的角度来看，神经机器翻译整体上更具有前沿性。本章主要介绍了统计机器翻译的开山之作—IBM模型，它主要讲了怎么使用词汇对机器翻译进行建模。IBM模型由Peter E. Brown等人在1993年提出，并详细阐述于论文—《The Mathematics of Statistical Machine Translation: Parameter Estimation》。这篇文章的视野和对问题的定义远超当时人所能看到的东西，其衍生出来的一系列方法和新的问题还被后人花费将近10年的时间来进行研究与讨论。
\section{什么是基于词的翻译模型}\index{Chapter3.1}%Index的作用，目前不清晰
\hspace{2em}在机器翻译中，我们希望得到一个源语句到目标语译文的翻译。但机器并不知道如何翻译。因此在做机器翻译的过程中，最初面临的一个问题是：如何对翻译进行建模？从计算机的角度看，建模的目的就是把抽象的问题转换为可计算的问题。所以机器翻译的一个核心问题是：如何将翻译转换为一个可计算的模型或过程。
\noindent\hspace{2em}基于单词的统计机器翻译模型又是如何描述翻译的呢？IBM模型提出了一个观点：在翻译源语句时，通常是把每个源语句的单词翻译成对应的目标语单词，然后调整这些单词的顺序，最后得到翻译结果。尽管在人看来基于单词的对应进行翻译是很自然的事，但是机器并不一定。
\noindent\hspace{2em}举个例子说明如何基于单词的对应进行翻译。如图 \ref{figureC3.1}所示，表示的是汉译英的例子。其中源语句是“我 对 你 感到 满意”。首先我们把源语句的单词“我”、“对”、“\\你”、“感到”和“满意”分别翻译为“I”、“with”、“you”、“am”和“satisfied”，然后调整单词的顺序，比如“am”放在译文的第2个位置，“you”应该放在最后的位置等，最后得到译文“I am satisfied with you”。
%空一行用来段落换行，noindent取消首行缩进，hspace{}指定缩进距离，1em等于两个英文字符|一个汉字
%----------------------------------------------
% 图3.1
\definecolor{ublue}{rgb}{0.152,0.250,0.545}
\begin{figure}[htp]
\centering
\definecolor{ublue}{rgb}{0.152,0.250,0.545}
\begin{tikzpicture}
\begin{scope}
\node [anchor=west] (s1) at (0,0) {我};
\node [anchor=west] (s2) at ([xshift=0.5em]s1.east) {对};
\node [anchor=west] (s3) at ([xshift=0.5em]s2.east) {你};
\node [anchor=west] (s4) at ([xshift=0.5em]s3.east) {感到};
\node [anchor=west] (s5) at ([xshift=0.5em]s4.east) {满意};
\end{scope}
\begin{scope}[yshift=-3em]
\node [anchor=west] (t1) at (0.35em,0) {I};
\node [anchor=west] (t2) at ([xshift=0.3em,yshift=-0.1em]t1.east) {am};
\node [anchor=west] (t3) at ([xshift=0.3em,yshift=0.1em]t2.east) {satisfied};
\node [anchor=west] (t4) at ([xshift=0.3em]t3.east) {with};
\node [anchor=west] (t5) at ([xshift=0.3em,yshift=-0.2em]t4.east) {you};
\end{scope}
{
\draw [-,thick,ublue,dashed] (s1.south) -- (t1.north);
\draw [-,thick,ublue,dashed] (s4.south) -- ([yshift=0.3em]t2.north);
\draw [-,thick,ublue,dashed] (s2.south) ..controls +(south:1em) and +(north:1em).. (t4.north);
\draw [-,thick,ublue,dashed] (s3.south) ..controls +(south:0.5em) and +(north:1.5em).. (t5.north);
\draw [-,thick,ublue,dashed] (s5.south) -- (t3.north);
}
\end{tikzpicture}
\caption{此处为图片的描述...例：基于词对应进行翻译.}
\label{figureC3.1}
\end{figure}
%-------------------------------------------
\noindent\hspace{2em}传统观点认为基于词的翻译过程包含如下三个步骤。如图 \ref{figureC3.2}所示。
\noindent\hspace{2em}第一、分析。将源语句或目标语句切分或者表示为能够处理的最小单元的过程。在基于词的翻译模型的最小处理单元就是单词。在这里也可以简单地将分析理解为分词。
\noindent\hspace{2em}第二、转换。把源语句中的每个单词都翻译成目标语单词。此处引用Figure \ref{figureC3.1}作为示例。
\noindent\hspace{2em}第三、生成。基于转换的结果，将目标语译文变成通顺且合乎语法的句子。
%----------------------------------------------
% 图3.2
\begin{figure}[htp]
\centering
\begin{tikzpicture}
\definecolor{ugreen}{rgb}{0,0.5,0}
\definecolor{ublue}{rgb}{0.152,0.250,0.545}
\node [anchor=west,draw,thick,minimum width=6.7em,minimum height=0.8em] (sent) at (0,0) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,red] (s1) at ([yshift=-1.5em]sent.south west) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,ugreen] (s2) at ([xshift=0.4em]s1.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,orange] (s3) at ([xshift=0.4em]s2.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,ublue] (s4) at ([xshift=0.4em]s3.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,purple] (s5) at ([xshift=0.4em]s4.east) {};
{
\node [anchor=west,draw,thick,circle,minimum size=0.3em,red,fill=red] (t1) at ([yshift=-2.0em]s1.west) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,ugreen,fill=ugreen] (t2) at ([xshift=0.4em]t1.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,orange,fill=orange] (t3) at ([xshift=0.4em]t2.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,ublue,fill=ublue] (t4) at ([xshift=0.4em]t3.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,purple,fill=purple] (t5) at ([xshift=0.4em]t4.east) {};
}
{
\node [anchor=west,draw,thick,circle,minimum size=0.3em,red,fill=red] (ft1) at ([yshift=-2.0em]t1.west) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,ublue,fill=ublue] (ft2) at ([xshift=0.4em]ft1.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,purple,fill=purple] (ft3) at ([xshift=0.4em]ft2.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,ugreen,fill=ugreen] (ft4) at ([xshift=0.4em]ft3.east) {};
\node [anchor=west,draw,thick,circle,minimum size=0.3em,orange,fill=orange] (ft5) at ([xshift=0.4em]ft4.east) {};
}
\draw [->,thick,double] ([yshift=-0.1em]sent.south) -- ([yshift=-0.8em]sent.south);
{
\draw [->,thick] ([yshift=-0.1em]s1.south) -- ([yshift=0.1em]t1.north);
\draw [->,thick] ([yshift=-0.1em]s2.south) -- ([yshift=0.1em]t2.north);
\draw [->,thick] ([yshift=-0.1em]s3.south) -- ([yshift=0.1em]t3.north);
\draw [->,thick] ([yshift=-0.1em]s4.south) -- ([yshift=0.1em]t4.north);
\draw [->,thick] ([yshift=-0.1em]s5.south) -- ([yshift=0.1em]t5.north);
}
{
\draw [->,thick] ([yshift=-0.1em]t1.south) -- ([yshift=0.1em]ft1.north);
\draw [->,thick] ([yshift=-0.1em]t2.south) -- ([yshift=0.1em]ft4.north);
\draw [->,thick] ([yshift=-0.1em]t3.south) -- ([yshift=0.1em]ft5.north);
\draw [->,thick] ([yshift=-0.1em]t4.south) -- ([yshift=0.1em]ft2.north);
\draw [->,thick] ([yshift=-0.1em]t5.south) -- ([yshift=0.1em]ft3.north);
}
{
\node [anchor=north west] (label1) at ([yshift=0.3em]sent.south east) {{\scriptsize \textbf{分析}}};
\node [anchor=north west] (label2) at ([yshift=-0.5em]label1.south west) {{\scriptsize \textbf{转换}}};
\node [anchor=north west] (label3) at ([yshift=-0.5em]label2.south west) {{\scriptsize \textbf{生成}}};
}
\end{tikzpicture}
\caption{此处为图片的描述...例：基于词的翻译过程.}
\label{figureC3.2}
\end{figure}
%---------------------------
\noindent\hspace{2em}从现在的角度看，分析、转换和生成依然是非常深刻的一个观点。该过程蕴含于很多的任务，比如句法分析。即使对于神经机器翻译，从大的框架来说，依然在做分析、转换和生成，只不过有些过程隐含在神经网络的设计中。
\section{构建一个简易机器翻译系统}\index{Chapter3.2}%Index的作用，目前不清晰
\noindent\hspace{2em}本节首先对比人工翻译流程和机器翻译流程的异同点，从中我们可以归纳出构建机器翻译系统的两个主要流程：训练和解码。其次从单词翻译概率、句子级翻译模型和解码三个方面描述如何构建一个简易机器翻译系统。其中单词翻译概率和句子级翻译模型属于训练流程。
\subsection{机器翻译的思路}\index{Chapter3.2.1}
\subsubsection{人工翻译流程}\index{Chapter3.2.1.1}
\noindent\hspace{2em}当我们翻译一个句子时，首先会快速地分析出句子中词的构成，然后基于以往的知识，得到每个词的候选翻译，最后利用对单词的理解拼出来一个译文。这描述了人在翻译时的基本过程。尽管并不是严格的从心理学或者脑科学得出来的观点，但至少可以帮助我们理解人的翻译。
%----------------------------------------------
% 图3.3
\begin{figure}[htp]
\centering
\definecolor{ublue}{rgb}{0.152,0.250,0.545}
\begin{tikzpicture}
\begin{scope}
\node [anchor=west] (s1) at (0,0) {\textbf{我}};
\node [anchor=west] (s2) at ([xshift=2em]s1.east) {\textbf{对}};
\node [anchor=west] (s3) at ([xshift=2em]s2.east) {\textbf{你}};
\node [anchor=west] (s4) at ([xshift=2em]s3.east) {\textbf{表示}};
\node [anchor=west] (s5) at ([xshift=2em]s4.east) {\textbf{满意}};
\node [anchor=south west] (sentlabel) at ([yshift=-0.5em]s1.north west) {\scriptsize{\textbf{\color{red}{待翻译句子(已经分词):}}}};
{
\draw [->,very thick,ublue] (s1.south) -- ([yshift=-0.7em]s1.south);
\draw [->,very thick,ublue] (s2.south) -- ([yshift=-0.7em]s2.south);
\draw [->,very thick,ublue] (s3.south) -- ([yshift=-0.7em]s3.south);
\draw [->,very thick,ublue] (s4.south) -- ([yshift=-0.7em]s4.south);
\draw [->,very thick,ublue] (s5.south) -- ([yshift=-0.7em]s5.south);
{\small
\node [anchor=north,inner sep=2pt,fill=red!20,minimum height=1.5em,minimum width=2.5em] (t11) at ([yshift=-1em]s1.south) {I};
\node [anchor=north,inner sep=2pt,fill=red!20,minimum height=1.5em,minimum width=2.5em] (t12) at ([yshift=-0.2em]t11.south) {me};
\node [anchor=north,inner sep=2pt,fill=red!20,minimum height=1.5em,minimum width=2.5em] (t13) at ([yshift=-0.2em]t12.south) {I'm};
\node [anchor=north west,inner sep=1pt,fill=black] (tl11) at (t11.north west) {\tiny{{\color{white} \textbf{1}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (tl12) at (t12.north west) {\tiny{{\color{white} \textbf{1}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (tl13) at (t13.north west) {\tiny{{\color{white} \textbf{1}}}};
\node [anchor=north,inner sep=2pt,fill=green!20,minimum height=1.5em,minimum width=2.5em] (t21) at ([yshift=-1em]s2.south) {to};
\node [anchor=north,inner sep=2pt,fill=green!20,minimum height=1.5em,minimum width=2.5em] (t22) at ([yshift=-0.2em]t21.south) {with};
\node [anchor=north,inner sep=2pt,fill=green!20,minimum height=1.5em,minimum width=2.5em] (t23) at ([yshift=-0.2em]t22.south) {for};
\node [anchor=north west,inner sep=1pt,fill=black] (tl21) at (t21.north west) {\tiny{{\color{white} \textbf{2}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (tl22) at (t22.north west) {\tiny{{\color{white} \textbf{2}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (tl23) at (t23.north west) {\tiny{{\color{white} \textbf{2}}}};
\node [anchor=north,inner sep=2pt,fill=blue!20,minimum height=1.5em,minimum width=2.5em] (t31) at ([yshift=-1em]s3.south) {you};
\node [anchor=north west,inner sep=1pt,fill=black] (tl31) at (t31.north west) {\tiny{{\color{white} \textbf{3}}}};
\node [anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t41) at ([yshift=-1em]s4.south) {$\phi$};
\node [anchor=north,inner sep=2pt,fill=orange!20,minimum height=1.5em,minimum width=3em] (t42) at ([yshift=-0.2em]t41.south) {show};
\node [anchor=north west,inner sep=1pt,fill=black] (tl41) at (t41.north west) {\tiny{{\color{white} \textbf{4}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (tl42) at (t42.north west) {\tiny{{\color{white} \textbf{4}}}};
\node [anchor=north,inner sep=2pt,fill=purple!20,minimum height=1.5em,minimum width=4.5em] (t51) at ([yshift=-1em]s5.south) {satisfy};
\node [anchor=north,inner sep=2pt,fill=purple!20,minimum height=1.5em,minimum width=4.5em] (t52) at ([yshift=-0.2em]t51.south) {satisfied};
\node [anchor=north,inner sep=2pt,fill=purple!20,minimum height=1.5em,minimum width=4.5em] (t53) at ([yshift=-0.2em]t52.south) {satisfies};
\node [anchor=north west,inner sep=1pt,fill=black] (tl51) at (t51.north west) {\tiny{{\color{white} \textbf{5}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (tl52) at (t52.north west) {\tiny{{\color{white} \textbf{5}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (tl53) at (t53.north west) {\tiny{{\color{white} \textbf{5}}}};
}
}
\end{scope}
\begin{scope}
{\small
{
\node [anchor=west,inner sep=2pt,fill=red!20,minimum height=1.5em,minimum width=2.5em] (ft11) at ([yshift=-1.2in]t11.west) {I'm};
\node [anchor=center,inner sep=2pt,fill=purple!20,minimum height=1.5em,minimum width=5em] (ft12) at ([xshift=5.0em]ft11.center) {satisfied};
\node [anchor=center,inner sep=2pt,fill=green!20,minimum height=1.5em,minimum width=2.5em] (ft13) at ([xshift=5.0em]ft12.center) {with};
\node [anchor=center,inner sep=2pt,fill=blue!20,minimum height=1.5em,minimum width=2.5em] (ft14) at ([xshift=4.0em]ft13.center) {you};
}
{
\node [anchor=north west,inner sep=1pt,fill=black] (ftl11) at (ft11.north west) {\tiny{{\color{white} \textbf{1}}}};
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\node [anchor=north west,inner sep=1pt,fill=black] (ftl13) at (ft13.north west) {\tiny{{\color{white} \textbf{2}}}};
\node [anchor=north west,inner sep=1pt,fill=black] (ftl14) at (ft14.north west) {\tiny{{\color{white} \textbf{3}}}};
}
{
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\node [anchor=east,inner sep=1pt] (nulltranslabel) at (t42.south west) {\scriptsize{\textbf{翻空}}};
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}
\end{scope}
\end{tikzpicture}
\caption{此处为图片的描述...例：人工翻译的过程}
\label{figureC3.3}
\end{figure}
%---------------------------
\noindent\hspace{2em}如图\ref{figureC3.3}所示，表示的是人工翻译的过程。其中待翻译的句子是“我 对 你表示 满意”，它的一个可能译文是“I’m satisfied with you”。我们可以把上述过程总结为如下两个过程。
\noindent\hspace{2em}第一、学习单词的翻译。即获取待翻译句子的每个单词的候选翻译。比如“对”的可能译文有“to”、“with”和“for”等。对于人来说，可以通过阅读、背诵、做题或者老师教等途径获得，并在大脑中形成知识。这些知识就包含了源语词与目标语词对应关系。我们也把这个过程称之为学习过程。
\noindent\hspace{2em}第二、运用知识生成译文。当翻译一个课本上没有见过的句子时，我们可能会想到词之间的翻译关系、一些常见的单词搭配、主谓宾等语法知识等，比如“satisfied”后面常使用介词“with”来表示对人的满意等，并基于这些知识快速地对译文进行生成。
\subsubsection{机器翻译流程}\index{Chapter3.2.1.2}
\noindent\hspace{2em}那么机器是如何完成翻译的呢？机器翻译可能没有人那么智能。因为一方面没有老师教给它一些非常好用的规则，使它能够快速得到译文。另一方面机器很笨，尽管它能知道每个单词的译文，但是不知道这些译文怎么拼装成句，甚至不知道哪些译文是对的。为了更加直观地理解机器在翻译时的困境，我们将其中需要解决的问题归纳如下。
\noindent\hspace{2em}问题一、如何将单词的译文拼装成句？
\noindent\hspace{2em}问题二、如果可以将译文成句，那如何判断结果的好坏？
\noindent\hspace{2em}对于问题一。机器最擅长的就是计算，它可以类似于走一条路径，尝试把译文拼装成句。如图3.4中蓝色和红色的线，表示的就是两条译文选择路径，区别在于“满意”和“对”的翻译候选是不一样的，蓝色线选择的是“satisfy”和“to”，而红色线是“satisfied”和“with”。换句话说，翻译就是一条译文选择路径，不同的译文对应不同的路径，并且词序也可以不同。机器可以利用它的计算能力找到很多这样的路径。
%-----------公式样例
\subsubsection{如何从一个双语平行数据中学习？}\index{Chapter3.2.3.2}
\noindent\hspace{2em}在上一节的骰子游戏中，我们重复地掷骰子很多次，然后计算“1”到“6”各出现的次数，再除以掷的总次数，最后得到它们出现的近似概率。我们同样用类似的方式估计单词翻译概率。假设$x$表示任意源语单词，所有的目标语单词$y \in Y$都可能是它的译文。当给定一个互译的句对$(s,t)$，我们定义$\textrm{P}(x \leftrightarrow y; s, t)$表示在$(s,t)$中$x$和$y$互译的可能性或概率。其中$x$属于句子$s$中的词，而$y$属于$t$中的词，具体计算如公式\ref{eqC3.1}\\所示。
\begin{equation}
\textrm{P}(x \leftrightarrow y; s,t) \equiv \textrm{P}(x,y;s,t) = \frac{c(x,y;s,t)}{\sum_{x',y'} c(x',y';s,t)}
\label{eqC3.1}
\end{equation}
\noindent\hspace{2em}上式中分子$c(x,y;s,t)$表示$x$和$y$在句对$(s,t)$中共现的总次数，\cite{Aho-Ullman:1969:JCSS}分母$\sum_{x',y'} c(x',y';$ $s,t)$表示任意的$x'$和任意的$y'$在$(s,t)$中共现的总次数。\cite{book_key}为了更加清晰的理解如何计算$c(x,y;s,t)$,我们用图3.7描述的算法进行了说明。
\noindent\hspace{2em}这是一个table的样例，word里目前还没有使用表格，因此借用学长论文里的table，如表\ref{table-ner}所示
%----------------------
\begin{table}[htp]
\small
\begin{center}
\begin{tabular}{l|l}
\hline \bf Model & \bf F1 \\
\hline
$best \ published$ \\
BiLSTM-CRF \cite{lample-etal-2016-neural} & 90.94 \\
BiLSTM-CRF+ELMo \cite{peters-etal-2018-deep} & 92.22 \\
BERT Base \cite{devlin2018bert} & 92.40 \\
BERT Large \cite{devlin2018bert} & 92.80 \\
BiLSTM-CRF+PCE \cite{Akbik2019PooledCE} & 93.18 \\
\hline
Random RNNs w/o pre-trained LM & 90.64 \\
DARTS w/o pre-trained LM & 91.05 \\
I-DARTS ($n=2$) w/o pre-trained LM & 90.96 \\
I-DARTS ($n=1$) w/o pre-trained LM & 91.23 \\
\hline
Random RNNs & 92.89 \\
DARTS & 93.13 \\
I-DARTS ($n=2$) & 93.14 \\
I-DARTS ($n=1$) & 93.47 \\
\hline
\end{tabular}
\end{center}
\caption{\label{font-table} F1 scores on the CoNLL-2003 English NER test set. }
\label{table-ner}
\end{table}
Book/Chapters/index.tex deleted 100644 → 0
%----------------------------------------------------------------------------------------
% INDEX
%----------------------------------------------------------------------------------------
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%----------------------------------------------------------------------------------------
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%----------------------------------------------------------------------------------------
% COPYRIGHT PAGE
%----------------------------------------------------------------------------------------
\newpage
~\vfill
\thispagestyle{empty}
\noindent Copyright \copyright\ 2019 John Smith\\ % Copyright notice
\noindent \textsc{Published by Publisher}\\ % Publisher
\noindent \textsc{book-website.com}\\ % URL
\noindent 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, March 2019} % Printing/edition date
%----------------------------------------------------------------------------------------
% TABLE OF CONTENTS
%----------------------------------------------------------------------------------------
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Book/mt-book-xelatex.idx
......@@ -113,14 +113,18 @@
\indexentry{Back Translation|hyperpage}{57}
\indexentry{迭代式回译|hyperpage}{58}
\indexentry{Iterative Back Translation|hyperpage}{58}
\indexentry{前向翻译|hyperpage}{58}
\indexentry{Forward Translation|hyperpage}{58}
\indexentry{前向翻译|hyperpage}{59}
\indexentry{Forward Translation|hyperpage}{59}
\indexentry{分布式表示|hyperpage}{59}
\indexentry{Distributed Representation|hyperpage}{59}
\indexentry{预训练|hyperpage}{59}
\indexentry{Pre-training|hyperpage}{59}
\indexentry{微调|hyperpage}{59}
\indexentry{Fine-tuning|hyperpage}{59}
\indexentry{多任务学习|hyperpage}{61}
\indexentry{Multitask Learning|hyperpage}{61}
\indexentry{知识精炼|hyperpage}{62}
\indexentry{Knowledge Distillation|hyperpage}{62}
\indexentry{模型压缩|hyperpage}{62}
\indexentry{Model Compression|hyperpage}{62}
\indexentry{学习难度|hyperpage}{62}
......@@ -136,10 +140,10 @@
\indexentry{中间层输出|hyperpage}{64}
\indexentry{Hint-based Knowledge Transfer|hyperpage}{64}
\indexentry{注意力分布|hyperpage}{64}
\indexentry{Attention To Attention Transfer|hyperpage}{64}
\indexentry{Attention To Attention Transfer|hyperpage}{65}
\indexentry{循环一致性|hyperpage}{67}
\indexentry{Circle Consistency|hyperpage}{67}
\indexentry{翻译中回译|hyperpage}{68}
\indexentry{On-the-fly Back-translation|hyperpage}{68}
\indexentry{网络结构搜索技术|hyperpage}{71}
\indexentry{Neural Architecture Search；NAS|hyperpage}{71}
\indexentry{神经架构搜索|hyperpage}{71}
\indexentry{Neural Architecture Search|hyperpage}{71}
Book/mt-book-xelatex.ptc
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Book/structure.tex
......@@ -48,6 +48,7 @@
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paper=b5paper, % Paper size, change to letterpaper for US letter size
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top=2cm, % Top margin
bottom=1.5cm, % Bottom margin
left=1.8cm, % Left margin
......
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