diff --git a/label_semantic_roles/README.en.md b/label_semantic_roles/README.en.md index d172d8b63de3f6c5a9016da4524a0a339d4696c9..3253d066e98ee44fdcc5ad296413541d70de1282 100644 --- a/label_semantic_roles/README.en.md +++ b/label_semantic_roles/README.en.md @@ -22,34 +22,20 @@ Standard SRL system mostly builds on top of Syntactic Analysis and contains five
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Fig 1. Syntactic parse tree
-核心关系-> HED -定中关系-> ATT -主谓关系-> SBV -状中结构-> ADV -介宾关系-> POB -右附加关系-> RAD -动宾关系-> VOB -标点-> WP - However, complete syntactic analysis requires identifying the relation among all constitutes and the performance of SRL is sensitive to the precision of syntactic analysis, which makes SRL a very challenging task. To reduce the complexity and obtain some syntactic structure information, we often use shallow syntactic analysis. Shallow Syntactic Analysis is also called partial parsing or chunking. Unlike complete syntactic analysis which requires the construction of the complete parsing tree, Shallow Syntactic Analysis only need to identify some independent components with relatively simple structure, such as verb phrases (chunk). To avoid difficulties in constructing a syntactic tree with high accuracy, some work\[[1](#Reference)\] proposed semantic chunking based SRL methods, which convert SRL as a sequence tagging problem. Sequence tagging tasks classify syntactic chunks using BIO representation. For syntactic chunks forming a chunk of type A, the first chunk receives the B-A tag (Begin), the remaining ones receive the tag I-A (Inside), and all chunks outside receive the tag O-A. The BIO representation of above example is shown in Fig.1.
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Fig 2. BIO represention
-输入序列-> input sequence -语块-> chunk -标注序列-> label sequence -角色-> role - This example illustrates the simplicity of sequence tagging because (1) shallow syntactic analysis reduces the precision requirement of syntactic analysis; (2) pruning candidate arguments is removed; 3) argument identification and tagging are finished at the same time. Such unified methods simplify the procedure, reduce the risk of accumulating errors and boost the performance further. In this tutorial, our SRL system is built as an end-to-end system via a neural network. We take only text sequences, without using any syntactic parsing results or complex hand-designed features. We give public dataset [CoNLL-2004 and CoNLL-2005 Shared Tasks](http://www.cs.upc.edu/~srlconll/) as an example to illustrate: given a sentence with predicates marked, identify the corresponding arguments and their semantic roles by sequence tagging method. @@ -71,13 +57,10 @@ The operation of a single LSTM cell contain 3 parts: (1) input-to-hidden: map in Fig.3 illustrate the final stacked recurrent neural networks.

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Fig 3. Stacked Recurrent Neural Networks

-线性变换-> linear transformation -输入层到隐层-> input-to-hidden - ### Bidirectional Recurrent Neural Network LSTMs can summarize the history of previous inputs seen up to now, but can not see the future. In most of NLP (natural language processing) tasks, the entire sentences are ready to use. Therefore, sequential learning might be much efficient if the future can be encoded as well like histories. @@ -86,15 +69,10 @@ To address the above drawbacks, we can design bidirectional recurrent neural net

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Fig 4. Bidirectional LSTMs

-线性变换-> linear transformation -输入层到隐层-> input-to-hidden -正向处理输出序列->process sequence in the forward direction -反向处理上一层序列-> process sequence from the previous layer in backward direction - 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/machine_translation/README.md) ### Conditional Random Field @@ -156,18 +134,10 @@ After modification, the model is as follows:
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Fig 6. DB-LSTM for SRL tasks
-论元-> argu -谓词-> pred -谓词上下文-> ctx-p -谓词上下文区域标记-> $m_r$ -输入-> input -原句-> sentence -反向LSTM-> LSTM Reverse - ## Data Preparation In the tutorial, we use [CoNLL 2005](http://www.cs.upc.edu/~srlconll/) SRL task open dataset as an example. It is important to note that the training set and development set of the CoNLL 2005 SRL task are not free to download after the competition. Currently, only the test set can be obtained, including 23 sections of the Wall Street Journal and three sections of the Brown corpus. In this tutorial, we use the WSJ corpus as the training dataset to explain the model. However, since the training set is small, if you want to train a usable neural network SRL system, consider paying for the full corpus.