update

Chapter17/Figures/figure-application-of-multimodal-machine-translation-to-multitask-learning.tex

@@ -4,8 +4,8 @@
 \node(x)[]{$x$};
 \node(encoder)[coder, above of = x,yshift=4em]{{编码器}};
 \node(decoder_left)[coder, above of = encoder, yshift=6em,fill=blue!25]{{解码器}};
-\node(y_hat)[above of = decoder_left, yshift=4em]{{$y'$}};
-\node(y)[above of = decoder_left, xshift=-6em]{{$y$}};
+\node(y_hat)[above of = decoder_left, yshift=4em]{{$y$}};
+\node(y)[above of = decoder_left, xshift=-6em]{{$y_{<}$}};
 \node(decoder_right)[coder, above of = encoder, xshift=11em,fill=yellow!25]{{解码器}};
 
 \node(figure)[draw=white,above of = decoder_right,yshift=6.5em,scale=0.25] {\includegraphics[width=0.62\textwidth]{./Chapter17/Figures/figure-bank-without-attention.png}};

Chapter17/Figures/figure-modeling-a-global-approach-to-visual-characteristics.tex

@@ -30,8 +30,8 @@
 \node[anchor=north,font=\Large](x) at ([xshift=2.5em,yshift=-3.4em]encoder.south){$\seq{x}$};
 
 \node[draw,anchor=south,rounded corners=2pt,minimum width=4.0cm,minimum height=2.2em,fill=blue!20](decoder) at ([yshift=2.6em,xshift=2.2em]cnn2.north){解码器};
-\node[anchor=north,font=\Large](y) at ([xshift=2.5em,yshift=-3.4em]decoder.south){$\seq{y}$};
-\node[anchor=south,font=\Large](y_1) at ([yshift=3em]decoder.north){$\seq{y}'$};
+\node[anchor=north,font=\Large](y) at ([xshift=2.5em,yshift=-3.4em]decoder.south){$\seq{y}_{<}$};
+\node[anchor=south,font=\Large](y_1) at ([yshift=3em]decoder.north){$\seq{y}$};
 
 \draw[->,thick] ([yshift=-2.1em]cnn1.south) -- ([yshift=-0.1em]cnn1.south);
 \draw[->,thick] ([yshift=-2.1em]cnn2.south) -- ([yshift=-0.1em]cnn2.south);

Chapter17/Figures/figure-the-encoder-explicitly-incorporates-semantic-information.tex

@@ -12,8 +12,8 @@
 \node(tree)[word, below of = sky, yshift=-0.75cm, fill=blue!15]{tree};
 \node(cir)[circle,thick, minimum width=0.6cm, xshift=8cm,  draw=black]{};
 \node(decoder)[rectangle, rounded corners, minimum height=2.2em,minimum width=4.3em, right of = cir,xshift=3cm, draw=black, fill=blue!25]{\large{解码器}};
-\node(yn_1)[below of = decoder,yshift=-2cm,scale=1.2]{$\rm y_{n-1}$};
-\node(yn_2)[above of = decoder,yshift=2cm,scale=1.2]{$\rm y'_{n-1}$(bank)};
+\node(yn_1)[below of = decoder,yshift=-2cm,scale=1.2]{$\rm y_{{<}_{n-1}}$};
+\node(yn_2)[above of = decoder,yshift=2cm,scale=1.2]{$\rm y_{n-1}$(bank)};
 
 \draw[->, thick]([xshift=0.1cm]figure.east)to([xshift=2cm]figure.east);
 \draw[-,thick]([xshift=-0.03cm]cir.east)to([xshift=0.03cm]cir.west);

Chapter17/chapter17.tex

@@ -383,7 +383,7 @@
 
 \subsection{图像到文本的翻译}
 
-\parinterval 图像到文本的转换也可以看作是广义上的翻译，简单来说，就是把图像作为了源语言的唯一输入，而输出是文本。其中，图像描述生成是最典型的图像到文本的翻译任务（{\color{red} 引用：Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures}）。虽然，这部分内容并不是本书的重点，不过为了保证多模态翻译内容的完整性，这里对相关技术进行简要介绍。图像描述有时也被称作图说话、图像字幕生成，它在图像检索、智能导盲、人机交互等领域有着广泛的应用场景。
+\parinterval 图像到文本的转换也可以看作是广义上的翻译，简单来说，就是把图像作为了源语言的唯一输入，而输出是文本。其中，图像描述生成是最典型的图像到文本的翻译任务\upcite{DBLP:conf/ijcai/BernardiCEEEIKM17}。虽然，这部分内容并不是本书的重点，不过为了保证多模态翻译内容的完整性，这里对相关技术进行简要介绍。图像描述有时也被称作图说话、图像字幕生成，它在图像检索、智能导盲、人机交互等领域有着广泛的应用场景。
 
 %----------------------------------------------------------------------------------------------------
 \begin{figure}[htp]

bibliography.bib

@@ -13118,6 +13118,23 @@ author    = {Zhuang Liu and
   publisher = {Annual Meeting of the Association for Computational Linguistics},
   year      = {2019}
 }
+@inproceedings{DBLP:conf/ijcai/BernardiCEEEIKM17,
+  author    = {Raffaella Bernardi and
+               Ruket {\c{C}}akici and
+               Desmond Elliott and
+               Aykut Erdem and
+               Erkut Erdem and
+               Nazli Ikizler{-}Cinbis and
+               Frank Keller and
+               Adrian Muscat and
+               Barbara Plank},
+  title     = {Automatic Description Generation from Images: {A} Survey of Models,
+               Datasets, and Evaluation Measures (Extended Abstract)},
+  pages     = {4970--4974},
+  publisher = {International Joint Conference on Artificial Intelligence},
+  year      = {2017}
+}
+
 %%%%% chapter 17------------------------------------------------------
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