Merge branch 'develop' of https://github.com/PaddlePaddle/book into review_chp06

01.fit_a_line/README.md

@@ -4,7 +4,7 @@ Let us begin the tutorial with a classical problem called Linear Regression \[[1
 The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
 
 ## Problem Setup
-Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity.
+Suppose we have a dataset of $n$ real estate properties. Each real estate property will be referred to as **homes** in this chapter for clarity.
 
 Each home is associated with $d$ attributes. The attributes describe characteristics such the number of rooms in the home, the number of schools or hospitals in the neighborhood, and the traffic condition nearby.
 
@@ -15,7 +15,7 @@ $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b,  i=1,\
 where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\].
 
 ## Results Demonstration
-We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of simlilar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the more precise the model predicts, the closer the point is to the dotted line.
+We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the more precise the model predicts, the closer the point is to the dotted line.
 <p align="center">
     <img src = "image/predictions_en.png" width=400><br/>
     Figure 1. Predicted Value V.S. Actual Value

01.fit_a_line/index.html

@@ -46,7 +46,7 @@ Let us begin the tutorial with a classical problem called Linear Regression \[[1
 The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
 
 ## Problem Setup
-Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity.
+Suppose we have a dataset of $n$ real estate properties. Each real estate property will be referred to as **homes** in this chapter for clarity.
 
 Each home is associated with $d$ attributes. The attributes describe characteristics such the number of rooms in the home, the number of schools or hospitals in the neighborhood, and the traffic condition nearby.
 
@@ -57,7 +57,7 @@ $$y_i = \omega_1x_{i,1} + \omega_2x_{i,2} + \ldots + \omega_dx_{i,d} + b,  i=1,\
 where $\vec{\omega}$ and $b$ are the model parameters we want to estimate. Once they are learned, we will be able to predict the price of a home, given the attributes associated with it. We call this model **Linear Regression**. In other words, we want to regress a value against several values linearly. In practice, a linear model is often too simplistic to capture the real relationships between the variables. Yet, because Linear Regression is easy to train and analyze, it has been applied to a large number of real problems. As a result, it is an important topic in many classic Statistical Learning and Machine Learning textbooks \[[2,3,4](#References)\].
 
 ## Results Demonstration
-We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of simlilar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the more precise the model predicts, the closer the point is to the dotted line.
+We first show the result of our model. The dataset [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) is used to train a linear model to predict the home prices in Boston. The figure below shows the predictions the model makes for some home prices. The $X$-axis represents the median value of the prices of similar homes within a bin, while the $Y$-axis represents the home value our linear model predicts. The dotted line represents points where $X=Y$. When reading the diagram, the more precise the model predicts, the closer the point is to the dotted line.
 <p align="center">
     <img src = "image/predictions_en.png" width=400><br/>
     Figure 1. Predicted Value V.S. Actual Value

06.understand_sentiment/README.md

@@ -164,7 +164,7 @@ def stacked_lstm_net(input_dim,
     """
     A Wrapper for sentiment classification task.
     This network uses a bi-directional recurrent network,
-    consisting of three LSTM layers. This configuration is 
+    consisting of three LSTM layers. This configuration is
     motivated from the following paper, but uses few layers.
         http://www.aclweb.org/anthology/P15-1109
     input_dim: here is word dictionary dimension.

06.understand_sentiment/index.html

@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # Sentiment Analysis
 
-The source codes of this section locates at [book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/06.understand_sentiment). First-time users may refer to PaddlePaddle for [Installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
+The source codes of this section is located at [book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/06.understand_sentiment). First-time users may refer to PaddlePaddle for [Installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
 
 ## Background
 
@@ -70,7 +70,7 @@ The model we used in this chapter uses **Convolutional Neural Networks** (**CNNs
 
 ### Revisit to the Convolutional Neural Networks for Texts (CNN)
 
-The convolutional neural network for texts is introduced in chapter [recommender_system](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system), let's have a brief overview.
+The convolutional neural network for texts is introduced in chapter [recommender_system](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system), here is a brief overview.
 
 CNN mainly contains convolution and pooling operation, with versatile combinations in various applications. We firstly apply the convolution operation: we apply the kernel in each window, extracting features. Convolving by the kernel at every window produces a feature map. Next, we apply *max pooling* over time to represent the whole sentence, which is the maximum element across the feature map. In real applications, we will apply multiple CNN kernels on the sentences. It can be implemented efficiently by concatenating the kernels together as a matrix. Also, we can use CNN kernels with different kernel size. Finally, concatenating the resulting features produces a fixed-length representation, which can be combined with a softmax to form the model for the sentiment analysis problem.
 
@@ -206,8 +206,8 @@ def stacked_lstm_net(input_dim,
     """
     A Wrapper for sentiment classification task.
     This network uses a bi-directional recurrent network,
-    consisting three LSTM layers. This configure refers to
-    the paper with following url, but use fewer layers.
+    consisting of three LSTM layers. This configuration is
+    motivated from the following paper, but uses few layers.
         http://www.aclweb.org/anthology/P15-1109
     input_dim: here is word dictionary dimension.
     class_dim: number of categories.

07.label_semantic_roles/README.md

 # Semantic Role Labeling
 
-The source code of this chapter is live on [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/07.label_semantic_roles).
+The source code of this chapter locates at [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/07.label_semantic_roles).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
 
@@ -14,7 +14,7 @@ In the following example of a Chinese sentence, "to encounter" is the predicate
 
 $$\mbox{[小明 Ming]}_{\mbox{Agent}}\mbox{[昨天 yesterday]}_{\mbox{Time}}\mbox{[晚上 evening]}_\mbox{Time}\mbox{在[公园 a park]}_{\mbox{Location}}\mbox{[遇到 to encounter]}_{\mbox{Predicate}}\mbox{了[小红 Hong]}_{\mbox{Patient}}\mbox{。}$$
 
-Instead of analyzing the semantic information, **Semantic Role Labeling** (**SRL**) identifies the relation between the predicate and the other constituents surrounding it. The predicate-argument structures are labeled as specific semantic roles. A wide range of natural language understanding tasks, including *information extraction*, *discourse analysis*, and *deepQA*. Research usually assumes a predicate of a sentence to be specified; the only task is to identify its arguments and their semantic roles.
+Instead of analyzing the semantic information, **Semantic Role Labeling** (**SRL**) identifies the relationship between the predicate and the other constituents surrounding it. The predicate-argument structures are labeled as specific semantic roles. A wide range of natural language understanding tasks, including *information extraction*, *discourse analysis*, and *deepQA*. Research usually assumes a predicate of a sentence to be specified; the only task is to identify its arguments and their semantic roles.
 
 Conventional SRL systems mostly build on top of syntactic analysis, usually consisting of five steps:
 
@@ -31,7 +31,7 @@ Fig 1. Syntax tree
 </div>
 
 
-However, a complete syntactic analysis requires identifying the relation among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out receive the tag O.
+However, a complete syntactic analysis requires identifying the relationship among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out will receive the tag O.
 
 The BIO representation of above example is shown in Fig.1.
 
@@ -50,7 +50,7 @@ In this tutorial, our SRL system is built as an end-to-end system via a neural n
 
 ## Model
 
-**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim model long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
+**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim modeling long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
 
 ### Stacked Recurrent Neural Network
 
@@ -77,9 +77,9 @@ Fig 3. Stacked Recurrent Neural Networks
 
 ### Bidirectional Recurrent Neural Network
 
-While LSTMs can summarize the history -- all the previous input seen up until now -- they can not see the future. Because most NLP (natural language processing) tasks provide the entirety of sentences, sequential learning can benefit from having the future encoded as well as the history.
+While LSTMs can summarize the history, they can not see the future. Because most NLP (natural language processing) tasks provide the entirety of sentences, sequential learning can benefit from having the future encoded as well as the history.
 
-To address, we can design a bidirectional recurrent neural network by making a minor modification. A higher LSTM layer can process the sequence in reversed direction with regards to its immediate lower LSTM layer, i.e., deep LSTM layers take turns to train on input sequences from left-to-right and right-to-left. Therefore, LSTM layers at time-step $t$ can see both histories and the future, starting from the second layer. Fig. 4 illustrates the bidirectional recurrent neural networks.
+To address this, we can design a bidirectional recurrent neural network by making a minor modification. A higher LSTM layer can process the sequence in reversed direction with regards to its immediate lower LSTM layer, i.e., deep LSTM layers take turns to train on input sequences from left-to-right and right-to-left. Therefore, LSTM layers at time-step $t$ can see both histories and the future, starting from the second layer. Fig. 4 illustrates the bidirectional recurrent neural networks.
 
 
 <p align="center">  
@@ -87,16 +87,16 @@ To address, we can design a bidirectional recurrent neural network by making a m
 Fig 4. Bidirectional LSTMs
 </p>
 
-Note that, this bidirectional RNNs is different with the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following tasks [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
+Note that, this bidirectional RNNs is different from the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following chapter [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
 
 ### Conditional Random Field (CRF)
 
-Typically, a neural network's lower layers learn representations while its very top layer learns the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes as input the representations provided by the last LSTM layer.
+Typically, a neural network's lower layers learn representations while its very top layer accomplishs the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes representations provided by the last LSTM layer as input.
 
 
 The CRF is an undirected probabilistic graph with nodes denoting random variables and edges denoting dependencies between these variables. In essence, CRFs learn the conditional probability $P(Y|X)$, where $X = (x_1, x_2, ... , x_n)$ are sequences of input and $Y = (y_1, y_2, ... , y_n)$ are label sequences; to decode, simply search through $Y$ for a sequence that maximizes the conditional probability $P(Y|X)$, i.e., $Y^* = \mbox{arg max}_{Y} P(Y | X)$。
 
-Sequence tagging tasks do not assume a lot of conditional independence, because they are only concerned with the input and the output being linear sequences. Thus, the graph model of sequence tagging tasks is usually a simple chain or line, which results in a **Linear-Chain Conditional Random Field**, shown in Fig.5.
+Sequence tagging tasks do not assume a lot of conditional independence, because they only concern about the input and the output being linear sequences. Thus, the graph model of sequence tagging tasks is usually a simple chain or line, which results in a **Linear-Chain Conditional Random Field**, shown in Fig.5.
 
 <p align="center">  
 <img src="./image/linear_chain_crf.png" width = "35%" align=center><br>
@@ -129,7 +129,7 @@ Given predicates and a sentence, SRL tasks aim to identify arguments of the give
  - expand input 1 into a sequence of the same length with input 2's sentence, using one-hot representation;
 2. Convert the one-hot sequences from step 1 to vector sequences via a word embedding's lookup table;
 3. Learn the representation of input sequences by taking vector sequences from step 2 as inputs;
-4. Take the representation from step 3 as input, label sequence as supervisory signal, and realize sequence tagging tasks.
+4. Take the representation from step 3 as input, label sequence as a supervisory signal, and realize sequence tagging tasks.
 
 Here, we propose some improvements by introducing two simple but effective features:
 
@@ -140,11 +140,11 @@ Here, we propose some improvements by introducing two simple but effective featu
 After these modifications, the model is as follows, as illustrated in Figure 6:
 
 1. Construct inputs
- - Input 1: word sequence. Input 2: predicate. Input 3: predicate context, extract $n$ words before and after predicate. Input 4: region mark sequence, where an entry is 1 if word is located in the predicate context region, 0 otherwise.
+ - Input 1: word sequence. Input 2: predicate. Input 3: predicate context, extract $n$ words before and after predicate. Input 4: region mark sequence, where an entry is 1 if the word is located in the predicate context region, 0 otherwise.
  - expand input 2~3 into sequences with the same length with input 1
 2. Convert input 1~4 to vector sequences via word embedding lookup tables; While input 1 and 3 shares the same lookup table, input 2 and 4 have separate lookup tables.
 3. Take the four vector sequences from step 2 as inputs to bidirectional LSTMs; Train the LSTMs to update representations.
-4. Take the representation from step 3 as input to CRF, label sequence as supervisory signal, and complete sequence tagging tasks.
+4. Take the representation from step 3 as input to CRF, label sequence as a supervisory signal, and complete sequence tagging tasks.
 
 
 <div  align="center">  
@@ -161,8 +161,8 @@ The original data includes a variety of information such as POS tagging, naming
 ```text
 conll05st-release/
 └── test.wsj
-    ├── props  # 标注结果
-    └── words  # 输入文本序列
+    ├── props  # label results
+    └── words  # text sequence
 ```
 
 The annotation information is derived from the results of Penn TreeBank\[[7](#references)\] and PropBank \[[8](# references)\]. The labeling of the PropBank is different from the labeling methods mentioned before, but shares with it the same underlying principle. For descriptions of the labeling, please refer to the paper \[[9](#references)\].
@@ -202,7 +202,7 @@ In addition to the data, we provide following resources:
 | word_dict | dictionary of input sentences, total 44068 words |
 | label_dict | dictionary of labels, total 106 labels |
 | predicate_dict | predicate dictionary, total 3162 predicates |
-| emb | a pre-trained word vector lookup table, 32-dimentional |
+| emb | a pre-trained word vector lookup table, 32-dimensional |
 
 We trained a language model on the English Wikipedia to get a word vector lookup table used to initialize the SRL model. While training the SRL model, the word vector lookup table is no longer updated. To learn more about the language model and the word vector lookup table, please refer to the tutorial [word vector](https://github.com/PaddlePaddle/book/blob/develop/04.word2vec/README.md). There are 995,000,000 tokens in the training corpus, and the dictionary size is 4900,000 words. In the CoNLL 2005 training corpus, 5% of the words are not in the 4900,000 words, and we see them all as unknown words, represented by `<unk>`.
 
@@ -484,7 +484,7 @@ trainer.train(
 
 ### Application
 
-Aftern training is done, we need to select an optimal model based one performance index to do inference. In this task, one can simply select the model with the least number of marks on the test set. The `paddle.layer.crf_decoding` layer is used in the inference, but its inputs does not include the ground truth label.
+When training is completed, we need to select an optimal model based one performance index to do inference. In this task, one can simply select the model with the least number of marks on the test set. The `paddle.layer.crf_decoding` layer is used in the inference, but its inputs do not include the ground truth label.
 
 ```python
 predict = paddle.layer.crf_decoding(

07.label_semantic_roles/index.html

@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # Semantic Role Labeling
 
-The source code of this chapter is live on [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/07.label_semantic_roles).
+The source code of this chapter locates at [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/07.label_semantic_roles).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
 
@@ -56,7 +56,7 @@ In the following example of a Chinese sentence, "to encounter" is the predicate
 
 $$\mbox{[小明 Ming]}_{\mbox{Agent}}\mbox{[昨天 yesterday]}_{\mbox{Time}}\mbox{[晚上 evening]}_\mbox{Time}\mbox{在[公园 a park]}_{\mbox{Location}}\mbox{[遇到 to encounter]}_{\mbox{Predicate}}\mbox{了[小红 Hong]}_{\mbox{Patient}}\mbox{。}$$
 
-Instead of analyzing the semantic information, **Semantic Role Labeling** (**SRL**) identifies the relation between the predicate and the other constituents surrounding it. The predicate-argument structures are labeled as specific semantic roles. A wide range of natural language understanding tasks, including *information extraction*, *discourse analysis*, and *deepQA*. Research usually assumes a predicate of a sentence to be specified; the only task is to identify its arguments and their semantic roles.
+Instead of analyzing the semantic information, **Semantic Role Labeling** (**SRL**) identifies the relationship between the predicate and the other constituents surrounding it. The predicate-argument structures are labeled as specific semantic roles. A wide range of natural language understanding tasks, including *information extraction*, *discourse analysis*, and *deepQA*. Research usually assumes a predicate of a sentence to be specified; the only task is to identify its arguments and their semantic roles.
 
 Conventional SRL systems mostly build on top of syntactic analysis, usually consisting of five steps:
 
@@ -73,7 +73,7 @@ Fig 1. Syntax tree
 </div>
 
 
-However, a complete syntactic analysis requires identifying the relation among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out receive the tag O.
+However, a complete syntactic analysis requires identifying the relationship among all constituents. Thus, the accuracy of SRL is sensitive to the preciseness of the syntactic analysis, making SRL challenging. To reduce its complexity and obtain some information on the syntactic structures, we often use *shallow syntactic analysis* a.k.a. partial parsing or chunking. Unlike complete syntactic analysis, which requires the construction of the complete parsing tree, *Shallow Syntactic Analysis* only requires identifying some independent constituents with relatively simple structures, such as verb phrases (chunk). To avoid difficulties in constructing a syntax tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking-based SRL methods, which reduces SRL into a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using **BIO representation**. For syntactic chunks forming role A, its first chunk receives the B-A tag (Begin) and the remaining ones receive the tag I-A (Inside); in the end, the chunks left out will receive the tag O.
 
 The BIO representation of above example is shown in Fig.1.
 
@@ -92,7 +92,7 @@ In this tutorial, our SRL system is built as an end-to-end system via a neural n
 
 ## Model
 
-**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim model long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
+**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim modeling long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
 
 ### Stacked Recurrent Neural Network
 
@@ -119,9 +119,9 @@ Fig 3. Stacked Recurrent Neural Networks
 
 ### Bidirectional Recurrent Neural Network
 
-While LSTMs can summarize the history -- all the previous input seen up until now -- they can not see the future. Because most NLP (natural language processing) tasks provide the entirety of sentences, sequential learning can benefit from having the future encoded as well as the history.
+While LSTMs can summarize the history, they can not see the future. Because most NLP (natural language processing) tasks provide the entirety of sentences, sequential learning can benefit from having the future encoded as well as the history.
 
-To address, we can design a bidirectional recurrent neural network by making a minor modification. A higher LSTM layer can process the sequence in reversed direction with regards to its immediate lower LSTM layer, i.e., deep LSTM layers take turns to train on input sequences from left-to-right and right-to-left. Therefore, LSTM layers at time-step $t$ can see both histories and the future, starting from the second layer. Fig. 4 illustrates the bidirectional recurrent neural networks.
+To address this, we can design a bidirectional recurrent neural network by making a minor modification. A higher LSTM layer can process the sequence in reversed direction with regards to its immediate lower LSTM layer, i.e., deep LSTM layers take turns to train on input sequences from left-to-right and right-to-left. Therefore, LSTM layers at time-step $t$ can see both histories and the future, starting from the second layer. Fig. 4 illustrates the bidirectional recurrent neural networks.
 
 
 <p align="center">  
@@ -129,16 +129,16 @@ To address, we can design a bidirectional recurrent neural network by making a m
 Fig 4. Bidirectional LSTMs
 </p>
 
-Note that, this bidirectional RNNs is different with the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following tasks [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
+Note that, this bidirectional RNNs is different from the one proposed by Bengio et al. in machine translation tasks \[[3](#Reference), [4](#Reference)\]. We will introduce another bidirectional RNNs in the following chapter [machine translation](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md)
 
 ### Conditional Random Field (CRF)
 
-Typically, a neural network's lower layers learn representations while its very top layer learns the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes as input the representations provided by the last LSTM layer.
+Typically, a neural network's lower layers learn representations while its very top layer accomplishs the final task. These principles can guide our problem-solving approaches. In SRL tasks, a **Conditional Random Field** (*CRF*) is built on top of the network in order to perform the final prediction to tag sequences. It takes representations provided by the last LSTM layer as input.
 
 
 The CRF is an undirected probabilistic graph with nodes denoting random variables and edges denoting dependencies between these variables. In essence, CRFs learn the conditional probability $P(Y|X)$, where $X = (x_1, x_2, ... , x_n)$ are sequences of input and $Y = (y_1, y_2, ... , y_n)$ are label sequences; to decode, simply search through $Y$ for a sequence that maximizes the conditional probability $P(Y|X)$, i.e., $Y^* = \mbox{arg max}_{Y} P(Y | X)$。
 
-Sequence tagging tasks do not assume a lot of conditional independence, because they are only concerned with the input and the output being linear sequences. Thus, the graph model of sequence tagging tasks is usually a simple chain or line, which results in a **Linear-Chain Conditional Random Field**, shown in Fig.5.
+Sequence tagging tasks do not assume a lot of conditional independence, because they only concern about the input and the output being linear sequences. Thus, the graph model of sequence tagging tasks is usually a simple chain or line, which results in a **Linear-Chain Conditional Random Field**, shown in Fig.5.
 
 <p align="center">  
 <img src="./image/linear_chain_crf.png" width = "35%" align=center><br>
@@ -171,7 +171,7 @@ Given predicates and a sentence, SRL tasks aim to identify arguments of the give
  - expand input 1 into a sequence of the same length with input 2's sentence, using one-hot representation;
 2. Convert the one-hot sequences from step 1 to vector sequences via a word embedding's lookup table;
 3. Learn the representation of input sequences by taking vector sequences from step 2 as inputs;
-4. Take the representation from step 3 as input, label sequence as supervisory signal, and realize sequence tagging tasks.
+4. Take the representation from step 3 as input, label sequence as a supervisory signal, and realize sequence tagging tasks.
 
 Here, we propose some improvements by introducing two simple but effective features:
 
@@ -182,11 +182,11 @@ Here, we propose some improvements by introducing two simple but effective featu
 After these modifications, the model is as follows, as illustrated in Figure 6:
 
 1. Construct inputs
- - Input 1: word sequence. Input 2: predicate. Input 3: predicate context, extract $n$ words before and after predicate. Input 4: region mark sequence, where an entry is 1 if word is located in the predicate context region, 0 otherwise.
+ - Input 1: word sequence. Input 2: predicate. Input 3: predicate context, extract $n$ words before and after predicate. Input 4: region mark sequence, where an entry is 1 if the word is located in the predicate context region, 0 otherwise.
  - expand input 2~3 into sequences with the same length with input 1
 2. Convert input 1~4 to vector sequences via word embedding lookup tables; While input 1 and 3 shares the same lookup table, input 2 and 4 have separate lookup tables.
 3. Take the four vector sequences from step 2 as inputs to bidirectional LSTMs; Train the LSTMs to update representations.
-4. Take the representation from step 3 as input to CRF, label sequence as supervisory signal, and complete sequence tagging tasks.
+4. Take the representation from step 3 as input to CRF, label sequence as a supervisory signal, and complete sequence tagging tasks.
 
 
 <div  align="center">  
@@ -203,8 +203,8 @@ The original data includes a variety of information such as POS tagging, naming
 ```text
 conll05st-release/
 └── test.wsj
-    ├── props  # 标注结果
-    └── words  # 输入文本序列
+    ├── props  # label results
+    └── words  # text sequence
 ```
 
 The annotation information is derived from the results of Penn TreeBank\[[7](#references)\] and PropBank \[[8](# references)\]. The labeling of the PropBank is different from the labeling methods mentioned before, but shares with it the same underlying principle. For descriptions of the labeling, please refer to the paper \[[9](#references)\].
@@ -244,7 +244,7 @@ In addition to the data, we provide following resources:
 | word_dict | dictionary of input sentences, total 44068 words |
 | label_dict | dictionary of labels, total 106 labels |
 | predicate_dict | predicate dictionary, total 3162 predicates |
-| emb | a pre-trained word vector lookup table, 32-dimentional |
+| emb | a pre-trained word vector lookup table, 32-dimensional |
 
 We trained a language model on the English Wikipedia to get a word vector lookup table used to initialize the SRL model. While training the SRL model, the word vector lookup table is no longer updated. To learn more about the language model and the word vector lookup table, please refer to the tutorial [word vector](https://github.com/PaddlePaddle/book/blob/develop/04.word2vec/README.md). There are 995,000,000 tokens in the training corpus, and the dictionary size is 4900,000 words. In the CoNLL 2005 training corpus, 5% of the words are not in the 4900,000 words, and we see them all as unknown words, represented by `<unk>`.
 
@@ -526,7 +526,7 @@ trainer.train(
 
 ### Application
 
-Aftern training is done, we need to select an optimal model based one performance index to do inference. In this task, one can simply select the model with the least number of marks on the test set. The `paddle.layer.crf_decoding` layer is used in the inference, but its inputs does not include the ground truth label.
+When training is completed, we need to select an optimal model based one performance index to do inference. In this task, one can simply select the model with the least number of marks on the test set. The `paddle.layer.crf_decoding` layer is used in the inference, but its inputs do not include the ground truth label.
 
 ```python
 predict = paddle.layer.crf_decoding(

08.machine_translation/README.md

@@ -6,24 +6,24 @@ The source codes is located at [book/machine_translation](https://github.com/Pad
 
 Machine translation (MT) leverages computers to translate from one language to another. The language to be translated is referred to as the source language, while the language to be translated into is referred to as the target language. Thus, Machine translation is the process of translating from the source language to the target language. It is one of the most important research topics in the field of natural language processing.
 
-Early machine translation systems are mainly rule-based i.e. they rely on a language expert to specify the translation rules between the two languages. It is quite difficult to cover all the rules used in one languge. So it is quite a challenge for language experts to specify all possible rules in two or more different languages. Hence, a major challenge in conventional machine translation has been the difficulty in obtaining a complete rule set \[[1](#References)\]。
+Early machine translation systems are mainly rule-based i.e. they rely on a language expert to specify the translation rules between the two languages. It is quite difficult to cover all the rules used in one language. So it is quite a challenge for language experts to specify all possible rules in two or more different languages. Hence, a major challenge in conventional machine translation has been the difficulty in obtaining a complete rule set \[[1](#References)\]。
 
 
 To address the aforementioned problems, statistical machine translation techniques have been developed. These techniques learn the translation rules from a large corpus, instead of being designed by a language expert. While these techniques overcome the bottleneck of knowledge acquisition, there are still quite a lot of challenges, for example:
 
-1. human designed features cannot cover all possible linguistic variations;
+1. Human designed features cannot cover all possible linguistic variations;
 
-2. it is difficult to use global features;
+2. It is difficult to use global features;
 
-3. the techniques heavily rely on pre-processing techniques like word alignment, word segmentation and tokenization, rule-extraction and syntactic parsing etc. The error introduced in any of these steps could accumulate and impact translation quality.
+3. The techniques heavily rely on pre-processing techniques like word alignment, word segmentation and tokenization, rule-extraction and syntactic parsing etc. The error introduced in any of these steps could accumulate and impact translation quality.
 
 
 
 The recent development of deep learning provides new solutions to these challenges. The two main categories for deep learning based machine translation techniques are:
 
-1. techniques based on the statistical machine translation system but with some key components improved with neural networks, e.g., language model, reordering model (please refer to the left part of Figure 1);
+1. Techniques based on the statistical machine translation system but with some key components improved with neural networks, e.g., language model, reordering model (please refer to the left part of Figure 1);
 
-2. techniques mapping from source language to target language directly using a neural network, or end-to-end neural machine translation (NMT).
+2. Techniques mapping from source language to target language directly using a neural network, or end-to-end neural machine translation (NMT).
 
 <p align="center">
 <img src="image/nmt_en.png" width=400><br/>
@@ -98,9 +98,9 @@ There are three steps for encoding a sentence:
 
 2. Word embedding as a representation in the low-dimensional semantic space: There are two problems with one-hot vector representation
 
-  * the dimensionality of the vector is typically large, leading to the curse of dimensionality;
+  * The dimensionality of the vector is typically large, leading to the curse of dimensionality;
 
-  * it is hard to capture the relationships between words, i.e., semantic similarities. Therefore, it is useful to project the one-hot vector into a low-dimensional semantic space as a dense vector with fixed dimensions, i.e., $s_i=Cw_i$ for the $i$-th word, with $C\epsilon R^{K\times \left | V \right |}$ as the projection matrix and $K$ is the dimensionality of the word embedding vector.
+  * It is hard to capture the relationships between words, i.e., semantic similarities. Therefore, it is useful to project the one-hot vector into a low-dimensional semantic space as a dense vector with fixed dimensions, i.e., $s_i=Cw_i$ for the $i$-th word, with $C\epsilon R^{K\times \left | V \right |}$ as the projection matrix and $K$ is the dimensionality of the word embedding vector.
 
 3. Encoding of the source sequence via RNN: This can be described mathematically as:
 
@@ -332,10 +332,10 @@ is_generating = False
 
 5. Training mode:
 
-   - word embedding from the target language trg_embedding is passed to `gru_decoder_with_attention` as current_word.
+   - Word embedding from the target language trg_embedding is passed to `gru_decoder_with_attention` as current_word.
    - `recurrent_group` calls `gru_decoder_with_attention` in a recurrent way
-   - the sequence of next words from the target language is used as label (lbl)
-   - multi-class cross-entropy (`classification_cost`) is used to calculate the cost
+   - The sequence of next words from the target language is used as label (lbl)
+   - Multi-class cross-entropy (`classification_cost`) is used to calculate the cost
 
    ```python
    if not is_generating:
@@ -365,7 +365,7 @@ is_generating = False
 
 6. Generating mode:
 
-   - the decoder predicts a next target word based on the the last generated target word. Embedding of the last generated word is automatically gotten by GeneratedInputs.
+   - The decoder predicts a next target word based on the the last generated target word. Embedding of the last generated word is automatically gotten by GeneratedInputs.
    - `beam_search` calls `gru_decoder_with_attention` in a recurrent way, to predict sequence id.
 
    ```python

08.machine_translation/index.html

@@ -48,24 +48,24 @@ The source codes is located at [book/machine_translation](https://github.com/Pad
 
 Machine translation (MT) leverages computers to translate from one language to another. The language to be translated is referred to as the source language, while the language to be translated into is referred to as the target language. Thus, Machine translation is the process of translating from the source language to the target language. It is one of the most important research topics in the field of natural language processing.
 
-Early machine translation systems are mainly rule-based i.e. they rely on a language expert to specify the translation rules between the two languages. It is quite difficult to cover all the rules used in one languge. So it is quite a challenge for language experts to specify all possible rules in two or more different languages. Hence, a major challenge in conventional machine translation has been the difficulty in obtaining a complete rule set \[[1](#References)\]。
+Early machine translation systems are mainly rule-based i.e. they rely on a language expert to specify the translation rules between the two languages. It is quite difficult to cover all the rules used in one language. So it is quite a challenge for language experts to specify all possible rules in two or more different languages. Hence, a major challenge in conventional machine translation has been the difficulty in obtaining a complete rule set \[[1](#References)\]。
 
 
 To address the aforementioned problems, statistical machine translation techniques have been developed. These techniques learn the translation rules from a large corpus, instead of being designed by a language expert. While these techniques overcome the bottleneck of knowledge acquisition, there are still quite a lot of challenges, for example:
 
-1. human designed features cannot cover all possible linguistic variations;
+1. Human designed features cannot cover all possible linguistic variations;
 
-2. it is difficult to use global features;
+2. It is difficult to use global features;
 
-3. the techniques heavily rely on pre-processing techniques like word alignment, word segmentation and tokenization, rule-extraction and syntactic parsing etc. The error introduced in any of these steps could accumulate and impact translation quality.
+3. The techniques heavily rely on pre-processing techniques like word alignment, word segmentation and tokenization, rule-extraction and syntactic parsing etc. The error introduced in any of these steps could accumulate and impact translation quality.
 
 
 
 The recent development of deep learning provides new solutions to these challenges. The two main categories for deep learning based machine translation techniques are:
 
-1. techniques based on the statistical machine translation system but with some key components improved with neural networks, e.g., language model, reordering model (please refer to the left part of Figure 1);
+1. Techniques based on the statistical machine translation system but with some key components improved with neural networks, e.g., language model, reordering model (please refer to the left part of Figure 1);
 
-2. techniques mapping from source language to target language directly using a neural network, or end-to-end neural machine translation (NMT).
+2. Techniques mapping from source language to target language directly using a neural network, or end-to-end neural machine translation (NMT).
 
 <p align="center">
 <img src="image/nmt_en.png" width=400><br/>
@@ -140,9 +140,9 @@ There are three steps for encoding a sentence:
 
 2. Word embedding as a representation in the low-dimensional semantic space: There are two problems with one-hot vector representation
 
-  * the dimensionality of the vector is typically large, leading to the curse of dimensionality;
+  * The dimensionality of the vector is typically large, leading to the curse of dimensionality;
 
-  * it is hard to capture the relationships between words, i.e., semantic similarities. Therefore, it is useful to project the one-hot vector into a low-dimensional semantic space as a dense vector with fixed dimensions, i.e., $s_i=Cw_i$ for the $i$-th word, with $C\epsilon R^{K\times \left | V \right |}$ as the projection matrix and $K$ is the dimensionality of the word embedding vector.
+  * It is hard to capture the relationships between words, i.e., semantic similarities. Therefore, it is useful to project the one-hot vector into a low-dimensional semantic space as a dense vector with fixed dimensions, i.e., $s_i=Cw_i$ for the $i$-th word, with $C\epsilon R^{K\times \left | V \right |}$ as the projection matrix and $K$ is the dimensionality of the word embedding vector.
 
 3. Encoding of the source sequence via RNN: This can be described mathematically as:
 
@@ -374,10 +374,10 @@ is_generating = False
 
 5. Training mode:
 
-   - word embedding from the target language trg_embedding is passed to `gru_decoder_with_attention` as current_word.
+   - Word embedding from the target language trg_embedding is passed to `gru_decoder_with_attention` as current_word.
    - `recurrent_group` calls `gru_decoder_with_attention` in a recurrent way
-   - the sequence of next words from the target language is used as label (lbl)
-   - multi-class cross-entropy (`classification_cost`) is used to calculate the cost
+   - The sequence of next words from the target language is used as label (lbl)
+   - Multi-class cross-entropy (`classification_cost`) is used to calculate the cost
 
    ```python
    if not is_generating:
@@ -407,7 +407,7 @@ is_generating = False
 
 6. Generating mode:
 
-   - the decoder predicts a next target word based on the the last generated target word. Embedding of the last generated word is automatically gotten by GeneratedInputs.
+   - The decoder predicts a next target word based on the the last generated target word. Embedding of the last generated word is automatically gotten by GeneratedInputs.
    - `beam_search` calls `gru_decoder_with_attention` in a recurrent way, to predict sequence id.
 
    ```python

serve/Dockerfile

-FROM paddlepaddle/paddle:latest-gpu
+FROM paddlepaddle/paddle
 
 ENV PARAMETER_TAR_PATH=/data/param.tar \
     TOPOLOGY_FILE_PATH=/data/inference_topology.pkl

serve/Dockerfile.gpu

+FROM paddlepaddle/paddle:latest-gpu
+
+ENV PARAMETER_TAR_PATH=/data/param.tar \
+    TOPOLOGY_FILE_PATH=/data/inference_topology.pkl
+ADD requirements.txt /root
+ADD main.py /root
+RUN pip install -r /root/requirements.txt
+CMD ["python", "/root/main.py"]

serve/README.md

@@ -95,7 +95,7 @@ To run the inference server with GPU support, please make sure you have
 first, and run:
 
 ```bash
-nvidia-docker run --name paddle_serve -v `pwd`:/data -d -p 8000:80 -e WITH_GPU=1 paddlepaddle/book:serve
+nvidia-docker run --name paddle_serve -v `pwd`:/data -d -p 8000:80 -e WITH_GPU=1 paddlepaddle/book:serve-gpu
 ```
 
 this command will start a server on port `8000`.
@@ -215,4 +215,5 @@ the docker image again.
 
 ```bash
 docker build -t paddlepaddle/book:serve .
+docker build -t paddlepaddle/book:serve-gpu -f Dockerfile.gpu .
 ```