diff --git a/demo/semantic_role_labeling/data/extract_dict_feature.py b/demo/semantic_role_labeling/data/extract_dict_feature.py
index 2982e54c665b41400aab0a893ff3c76335404988..daca5f01cf2b3bd231bf530f17ec760272ce93e0 100644
--- a/demo/semantic_role_labeling/data/extract_dict_feature.py
+++ b/demo/semantic_role_labeling/data/extract_dict_feature.py
@@ -17,24 +17,15 @@ import os
 from optparse import OptionParser
 
 
-def extract_dict_features(pair_file, feature_file, src_dict_file,
-                          tgt_dict_file):
-    src_dict = set()
-    tgt_dict = set()
-
-    with open(pair_file) as fin, open(feature_file, 'w') as feature_out, open(
-            src_dict_file, 'w') as src_dict_out, open(tgt_dict_file,
-                                                      'w') as tgt_dict_out:
+def extract_dict_features(pair_file, feature_file):
+
+    with open(pair_file) as fin, open(feature_file, 'w') as feature_out:
         for line in fin:
-            sentence, labels = line.strip().split('\t')
+            sentence, predicate, labels = line.strip().split('\t')
             sentence_list = sentence.split()
             labels_list = labels.split()
 
-            src_dict.update(sentence_list)
-            tgt_dict.update(labels_list)
-
             verb_index = labels_list.index('B-V')
-            verb_feature = sentence_list[verb_index]
 
             mark = [0] * len(labels_list)
             if verb_index > 0:
@@ -42,47 +33,50 @@ def extract_dict_features(pair_file, feature_file, src_dict_file,
                 ctx_n1 = sentence_list[verb_index - 1]
             else:
                 ctx_n1 = 'bos'
-            ctx_n1_feature = ctx_n1
+            
+            if verb_index > 1:
+                mark[verb_index - 2] = 1
+                ctx_n2 = sentence_list[verb_index - 2]
+            else:
+                ctx_n2 = 'bos'
 
             mark[verb_index] = 1
-            ctx_0_feature = sentence_list[verb_index]
+            ctx_0 = sentence_list[verb_index]
 
             if verb_index < len(labels_list) - 2:
                 mark[verb_index + 1] = 1
                 ctx_p1 = sentence_list[verb_index + 1]
             else:
                 ctx_p1 = 'eos'
-            ctx_p1_feature = ctx_p1
+            
+            if verb_index < len(labels_list) - 3:
+                mark[verb_index + 2] = 1
+                ctx_p2 = sentence_list[verb_index + 2]
+            else:
+                ctx_p2 = 'eos'
+
 
             feature_str  = sentence + '\t' \
-                           + verb_feature + '\t' \
-                           + ctx_n1_feature + '\t' \
-                           + ctx_0_feature + '\t' \
-                           + ctx_p1_feature + '\t' \
+                           + predicate + '\t' \
+                           + ctx_n2 + '\t' \
+                           + ctx_n1 + '\t' \
+                           + ctx_0 + '\t' \
+                           + ctx_p1 + '\t' \
+                           + ctx_p2 + '\t' \
                            + ' '.join([str(i) for i in mark]) + '\t' \
                            + labels
 
             feature_out.write(feature_str + '\n')
 
-        src_dict_out.write('<unk>\n')
-        src_dict_out.write('\n'.join(list(src_dict)))
-
-        tgt_dict_out.write('\n'.join(list(tgt_dict)))
 
 
 if __name__ == '__main__':
 
-    usage = '-p pair_file -f feature_file -s source dictionary -t target dictionary '
+    usage = '-p pair_file -f feature_file'
     parser = OptionParser(usage)
     parser.add_option('-p', dest='pair_file', help='the pair file')
-    parser.add_option(
-        '-f', dest='feature_file', help='the file to store feature')
-    parser.add_option(
-        '-s', dest='src_dict', help='the file to store source dictionary')
-    parser.add_option(
-        '-t', dest='tgt_dict', help='the file to store target dictionary')
+    parser.add_option('-f', dest='feature_file', help='the feature file')
 
     (options, args) = parser.parse_args()
 
-    extract_dict_features(options.pair_file, options.feature_file,
-                          options.src_dict, options.tgt_dict)
+    extract_dict_features(options.pair_file, options.feature_file)
diff --git a/demo/semantic_role_labeling/data/extract_pairs.py b/demo/semantic_role_labeling/data/extract_pairs.py
index 4d1bef8f958a62be9941d474a0b67542dcc5cfab..86ab00ce41723169de035a841d9e129a1b9e82a3 100644
--- a/demo/semantic_role_labeling/data/extract_pairs.py
+++ b/demo/semantic_role_labeling/data/extract_pairs.py
@@ -51,7 +51,7 @@ def read_sentences(words_file):
         for line in fin:
             line = line.strip()
             if line == '':
-                sentences.append(s.lower())
+                sentences.append(s)
                 s = ''
             else:
                 s += line + ' '
@@ -64,6 +64,11 @@ def transform_labels(sentences, labels):
         if len(labels[i]) == 1:
             continue
         else:
+            verb_list = []
+            for x in labels[i][0]:
+                if x !='-':
+                   verb_list.append(x)
+
             for j in xrange(1, len(labels[i])):
                 label_list = labels[i][j]
                 current_tag = 'O'
@@ -88,8 +93,7 @@ def transform_labels(sentences, labels):
                         is_in_bracket = True
                     else:
                         print 'error:', ll
-
-                sen_lab_pair.append((sentences[i], label_seq))
+                sen_lab_pair.append((sentences[i], verb_list[j-1], label_seq))
     return sen_lab_pair
 
 
@@ -97,9 +101,9 @@ def write_file(sen_lab_pair, output_file):
     with open(output_file, 'w') as fout:
         for x in sen_lab_pair:
             sentence = x[0]
-            label_seq = ' '.join(x[1])
-            assert len(sentence.split()) == len(x[1])
-            fout.write(sentence + '\t' + label_seq + '\n')
+            label_seq = ' '.join(x[2])
+            assert len(sentence.split()) == len(x[2])
+            fout.write(sentence + '\t' + x[1]+'\t' +label_seq + '\n')
 
 
 if __name__ == '__main__':
diff --git a/demo/semantic_role_labeling/data/get_data.sh b/demo/semantic_role_labeling/data/get_data.sh
index 268c0995e27006ec62f38bdda9b0a0994dab096c..55e33f4685627ed483aa6642c518a33558091531 100644
--- a/demo/semantic_role_labeling/data/get_data.sh
+++ b/demo/semantic_role_labeling/data/get_data.sh
@@ -14,6 +14,10 @@
 # limitations under the License.
 set -e
 wget http://www.cs.upc.edu/~srlconll/conll05st-tests.tar.gz
+wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/verbDict.txt --no-check-certificate
+wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/targetDict.txt --no-check-certificate
+wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/wordDict.txt --no-check-certificate
+wget https://www.googledrive.com/host/0B7Q8d52jqeI9ejh6Q1RpMTFQT1k/semantic_role_labeling/emb --no-check-certificate
 tar -xzvf conll05st-tests.tar.gz
 rm conll05st-tests.tar.gz
 cp ./conll05st-release/test.wsj/words/test.wsj.words.gz  .
@@ -22,4 +26,4 @@ gunzip test.wsj.words.gz
 gunzip test.wsj.props.gz
 
 python extract_pairs.py  -w test.wsj.words -p test.wsj.props -o test.wsj.seq_pair
-python extract_dict_feature.py -p test.wsj.seq_pair -f feature  -s src.dict  -t tgt.dict
+python extract_dict_feature.py -p test.wsj.seq_pair -f feature 
diff --git a/demo/semantic_role_labeling/dataprovider.py b/demo/semantic_role_labeling/dataprovider.py
index 2ef25c42c1794c410fe85fd497a6ed9d2295dca9..c1c6eeca51773038e87af638e4afbac6d8472ac8 100644
--- a/demo/semantic_role_labeling/dataprovider.py
+++ b/demo/semantic_role_labeling/dataprovider.py
@@ -17,11 +17,15 @@ from paddle.trainer.PyDataProvider2 import *
 UNK_IDX = 0
 
 
-def hook(settings, word_dict, label_dict, **kwargs):
+def hook(settings, word_dict, label_dict, predicate_dict, **kwargs):
     settings.word_dict = word_dict
     settings.label_dict = label_dict
+    settings.predicate_dict = predicate_dict
+   
     #all inputs are integral and sequential type
     settings.slots = [
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(predicate_dict)),
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
@@ -31,27 +35,33 @@ def hook(settings, word_dict, label_dict, **kwargs):
         integer_value_sequence(len(label_dict))]
 
 
-@provider(init_hook=hook)
-def process(obj, file_name):
+def get_batch_size(yeild_data):
+    return len(yeild_data[0])
+    
+
+@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size, 
+          can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
+def process(settings, file_name):
     with open(file_name, 'r') as fdata:
         for line in fdata:
-            sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = \
+            sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2,  mark, label = \
                 line.strip().split('\t')
-
+           
             words = sentence.split()
             sen_len = len(words)
-            word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words]
+            word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
 
-            predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len
-            ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
-            ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX)] * sen_len
-            ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
+            predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
+            ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
+            ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
+            ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
+            ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
+            ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
 
             marks = mark.split()
             mark_slot = [int(w) for w in marks]
 
             label_list = label.split()
-            label_slot = [obj.label_dict.get(w) for w in label_list]
-
-            yield word_slot, predicate_slot, ctx_n1_slot, \
-                  ctx_0_slot, ctx_p1_slot, mark_slot, label_slot
+            label_slot = [settings.label_dict.get(w) for w in label_list]
+            yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
+                  ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
diff --git a/demo/semantic_role_labeling/db_lstm.py b/demo/semantic_role_labeling/db_lstm.py
index 364460afbe31caf42cd4f0836eba75e444b3f5b8..68feb9c4a77eb7d60180c2b3dbfc7ac2367cdb67 100644
--- a/demo/semantic_role_labeling/db_lstm.py
+++ b/demo/semantic_role_labeling/db_lstm.py
@@ -19,8 +19,9 @@ import sys
 from paddle.trainer_config_helpers import *
 
 #file paths
-word_dict_file = './data/src.dict'
-label_dict_file = './data/tgt.dict'
+word_dict_file = './data/wordDict.txt'
+label_dict_file = './data/targetDict.txt'
+predicate_file= './data/verbDict.txt'
 train_list_file = './data/train.list'
 test_list_file = './data/test.list'
 
@@ -31,8 +32,10 @@ if not is_predict:
     #load dictionaries
     word_dict = dict()
     label_dict = dict()
+    predicate_dict = dict()
     with open(word_dict_file, 'r') as f_word, \
-         open(label_dict_file, 'r') as f_label:
+         open(label_dict_file, 'r') as f_label, \
+         open(predicate_file, 'r') as f_pre:
         for i, line in enumerate(f_word):
             w = line.strip()
             word_dict[w] = i
@@ -41,6 +44,11 @@ if not is_predict:
             w = line.strip()
             label_dict[w] = i
 
+        for i, line in enumerate(f_pre):
+            w = line.strip()
+            predicate_dict[w] = i
+
+
     if is_test:
         train_list_file = None 
 
@@ -51,91 +59,169 @@ if not is_predict:
         module='dataprovider',
         obj='process',
         args={'word_dict': word_dict,
-              'label_dict': label_dict})
+              'label_dict': label_dict,
+              'predicate_dict': predicate_dict })
 
     word_dict_len = len(word_dict)
     label_dict_len = len(label_dict)
+    pred_len = len(predicate_dict)
 
 else:
     word_dict_len = get_config_arg('dict_len', int)
     label_dict_len = get_config_arg('label_len', int)
+    pred_len = get_config_arg('pred_len', int)
 
+############################## Hyper-parameters ##################################
 mark_dict_len = 2
 word_dim = 32
 mark_dim = 5
-hidden_dim = 128
+hidden_dim = 512
 depth = 8
-emb_lr = 1e-2
-fc_lr = 1e-2
-lstm_lr = 2e-2
+
+
+
+########################### Optimizer #######################################
+
 
 settings(
     batch_size=150,
-    learning_method=AdamOptimizer(),
-    learning_rate=1e-3,
+    learning_method=MomentumOptimizer(momentum=0),
+    learning_rate=2e-2,
     regularization=L2Regularization(8e-4),
-    gradient_clipping_threshold=25)
+    is_async=False,
+    model_average=ModelAverage(average_window=0.5,
+                               max_average_window=10000),
+                               
+)
 
-#6 features
+
+
+
+####################################### network ##############################
+#8 features and 1 target
 word = data_layer(name='word_data', size=word_dict_len)
-predicate = data_layer(name='verb_data', size=word_dict_len)
+predicate = data_layer(name='verb_data', size=pred_len)
+
+ctx_n2 = data_layer(name='ctx_n2_data', size=word_dict_len)
 ctx_n1 = data_layer(name='ctx_n1_data', size=word_dict_len)
 ctx_0 = data_layer(name='ctx_0_data', size=word_dict_len)
 ctx_p1 = data_layer(name='ctx_p1_data', size=word_dict_len)
+ctx_p2 = data_layer(name='ctx_p2_data', size=word_dict_len)
 mark = data_layer(name='mark_data', size=mark_dict_len)
 
+
 if not is_predict:
     target = data_layer(name='target', size=label_dict_len)
 
-ptt = ParameterAttribute(name='src_emb', learning_rate=emb_lr)
-layer_attr = ExtraLayerAttribute(drop_rate=0.5)
-fc_para_attr = ParameterAttribute(learning_rate=fc_lr)
-lstm_para_attr = ParameterAttribute(initial_std=0., learning_rate=lstm_lr)
-para_attr = [fc_para_attr, lstm_para_attr]
 
-word_embedding = embedding_layer(size=word_dim, input=word, param_attr=ptt)
-predicate_embedding = embedding_layer(
-    size=word_dim, input=predicate, param_attr=ptt)
-ctx_n1_embedding = embedding_layer(size=word_dim, input=ctx_n1, param_attr=ptt)
-ctx_0_embedding = embedding_layer(size=word_dim, input=ctx_0, param_attr=ptt)
-ctx_p1_embedding = embedding_layer(size=word_dim, input=ctx_p1, param_attr=ptt)
-mark_embedding = embedding_layer(size=mark_dim, input=mark)
+default_std=1/math.sqrt(hidden_dim)/3.0
+
+emb_para = ParameterAttribute(name='emb', initial_std=0., learning_rate=0.)
+std_0 = ParameterAttribute(initial_std=0.)
+std_default = ParameterAttribute(initial_std=default_std) 
+
+word_embedding = embedding_layer(size=word_dim, input=word, param_attr=emb_para)
+predicate_embedding = embedding_layer(size=word_dim, input=predicate, param_attr=ParameterAttribute(name='vemb',initial_std=default_std))
+ctx_n2_embedding = embedding_layer(size=word_dim, input=ctx_n2, param_attr=emb_para)
+ctx_n2_embedding = embedding_layer(size=word_dim, input=ctx_n2, param_attr=emb_para)
+ctx_n1_embedding = embedding_layer(size=word_dim, input=ctx_n1, param_attr=emb_para)
+ctx_0_embedding = embedding_layer(size=word_dim, input=ctx_0, param_attr=emb_para)
+ctx_p1_embedding = embedding_layer(size=word_dim, input=ctx_p1, param_attr=emb_para)
+ctx_p2_embedding = embedding_layer(size=word_dim, input=ctx_p2, param_attr=emb_para)
+mark_embedding = embedding_layer(name='word_ctx-in_embedding', size=mark_dim, input=mark, param_attr=std_0)
+
 
 hidden_0 = mixed_layer(
+    name='hidden0',
     size=hidden_dim,
+    bias_attr=std_default,
     input=[
-        full_matrix_projection(input=word_embedding),
-        full_matrix_projection(input=predicate_embedding),
-        full_matrix_projection(input=ctx_n1_embedding),
-        full_matrix_projection(input=ctx_0_embedding),
-        full_matrix_projection(input=ctx_p1_embedding),
-        full_matrix_projection(input=mark_embedding),
+        full_matrix_projection(input=word_embedding, param_attr=std_default),
+        full_matrix_projection(input=predicate_embedding, param_attr=std_default),
+        full_matrix_projection(input=ctx_n2_embedding, param_attr=std_default),
+        full_matrix_projection(input=ctx_n1_embedding, param_attr=std_default),
+        full_matrix_projection(input=ctx_0_embedding, param_attr=std_default),
+        full_matrix_projection(input=ctx_p1_embedding, param_attr=std_default),
+        full_matrix_projection(input=ctx_p2_embedding, param_attr=std_default),
+        full_matrix_projection(input=mark_embedding, param_attr=std_default)
     ])
 
-lstm_0 = lstmemory(input=hidden_0, layer_attr=layer_attr)
+
+mix_hidden_lr = 1e-3
+lstm_para_attr = ParameterAttribute(initial_std=0.0, learning_rate=1.0)
+hidden_para_attr = ParameterAttribute(initial_std=default_std, learning_rate=mix_hidden_lr)
+
+lstm_0 = lstmemory(name='lstm0',
+                   input=hidden_0, 
+                   act=ReluActivation(),
+                   gate_act=SigmoidActivation(),
+                   state_act=SigmoidActivation(),
+                   bias_attr=std_0,
+                   param_attr=lstm_para_attr)
 
 #stack L-LSTM and R-LSTM with direct edges
 input_tmp = [hidden_0, lstm_0]
 
+
 for i in range(1, depth):
 
-    fc = fc_layer(input=input_tmp, size=hidden_dim, param_attr=para_attr)
+    mix_hidden = mixed_layer(name='hidden'+str(i),
+                             size=hidden_dim, 
+                             bias_attr=std_default,
+                             input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
+                                    full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
+                                   ]
+                             )
+
+    lstm = lstmemory(name='lstm'+str(i),
+                     input=mix_hidden,
+                     act=ReluActivation(),
+                     gate_act=SigmoidActivation(),
+                     state_act=SigmoidActivation(),
+                     reverse=((i % 2)==1),
+                     bias_attr=std_0,
+                     param_attr=lstm_para_attr)
+
+    input_tmp = [mix_hidden, lstm]
+
+feature_out = mixed_layer(name='output',
+                          size=label_dict_len,
+                          bias_attr=std_default, 
+                          input=[full_matrix_projection(input=input_tmp[0], param_attr=hidden_para_attr),
+                                 full_matrix_projection(input=input_tmp[1], param_attr=lstm_para_attr)
+                                ],
+                          )
 
-    lstm = lstmemory(
-        input=fc,
-        act=ReluActivation(),
-        reverse=(i % 2) == 1,
-        layer_attr=layer_attr)
-    input_tmp = [fc, lstm]
 
-prob = fc_layer(
-    input=input_tmp,
-    size=label_dict_len,
-    act=SoftmaxActivation(),
-    param_attr=para_attr)
 
 if not is_predict:
-    cls = classification_cost(input=prob, label=target)
-    outputs(cls)
+    crf_l = crf_layer( name = 'crf',
+                       size = label_dict_len,
+                       input = feature_out, 
+                       label = target,
+                       param_attr=ParameterAttribute(name='crfw',initial_std=default_std, learning_rate=mix_hidden_lr)
+
+                      )
+
+    
+    crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
+                                   size = label_dict_len,
+                                   input = feature_out,
+                                   label = target,
+                                   param_attr=ParameterAttribute(name='crfw')
+                                       )
+
+
+    eval = sum_evaluator(input=crf_dec_l)
+        
+    outputs(crf_l)
+
 else:
-    outputs(prob)
+    crf_dec_l = crf_decoding_layer(name = 'crf_dec_l',
+                                   size = label_dict_len,
+                                   input = feature_out,
+                                   param_attr=ParameterAttribute(name='crfw')
+                                       )
+
+    outputs(crf_dec_l)
+
diff --git a/demo/semantic_role_labeling/predict.py b/demo/semantic_role_labeling/predict.py
index 9a27112828e449174e3da79dc7db9fed20bfed6f..80183300f2a53948adfd0f0635706f4368c43205 100644
--- a/demo/semantic_role_labeling/predict.py
+++ b/demo/semantic_role_labeling/predict.py
@@ -26,7 +26,7 @@ UNK_IDX = 0
 
 
 class Prediction():
-    def __init__(self, train_conf, dict_file, model_dir, label_file):
+    def __init__(self, train_conf, dict_file, model_dir, label_file, predicate_dict_file):
         """
         train_conf: trainer configure.
         dict_file: word dictionary file name.
@@ -35,16 +35,19 @@ class Prediction():
 
         self.dict = {}
         self.labels = {}
+        self.predicate_dict={}
         self.labels_reverse = {}
-        self.load_dict_label(dict_file, label_file)
+        self.load_dict_label(dict_file, label_file, predicate_dict_file)
 
         len_dict = len(self.dict)
         len_label = len(self.labels)
+        len_pred = len(self.predicate_dict)
 
         conf = parse_config(
             train_conf,
-            'dict_len=' + str(len_dict) +
+            'dict_len=' + str(len_dict) + 
             ',label_len=' + str(len_label) +
+            ',pred_len=' + str(len_pred) +
             ',is_predict=True')
         self.network = swig_paddle.GradientMachine.createFromConfigProto(
             conf.model_config)
@@ -52,15 +55,17 @@ class Prediction():
 
         slots = [
             integer_value_sequence(len_dict),
+            integer_value_sequence(len_pred),
             integer_value_sequence(len_dict),
             integer_value_sequence(len_dict),
             integer_value_sequence(len_dict),
             integer_value_sequence(len_dict),
+            integer_value_sequence(len_dict), 
             integer_value_sequence(2)
-        ]
+            ]
         self.converter = DataProviderConverter(slots)
 
-    def load_dict_label(self, dict_file, label_file):
+    def load_dict_label(self, dict_file, label_file, predicate_dict_file):
         """
         Load dictionary from self.dict_file.
         """
@@ -71,39 +76,42 @@ class Prediction():
             self.labels[line.strip()] = line_count
             self.labels_reverse[line_count] = line.strip()
 
+        for line_count, line in enumerate(open(predicate_dict_file, 'r')):
+            self.predicate_dict[line.strip()] = line_count
     def get_data(self, data_file):
         """
         Get input data of paddle format.
         """
         with open(data_file, 'r') as fdata:
             for line in fdata:
-                sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip(
+                sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark, label = line.strip(
                 ).split('\t')
                 words = sentence.split()
                 sen_len = len(words)
-
+                 
                 word_slot = [self.dict.get(w, UNK_IDX) for w in words]
-                predicate_slot = [self.dict.get(predicate, UNK_IDX)] * sen_len
+                predicate_slot = [self.predicate_dict.get(predicate, UNK_IDX)] * sen_len
+                ctx_n2_slot = [self.dict.get(ctx_n2, UNK_IDX)] * sen_len
                 ctx_n1_slot = [self.dict.get(ctx_n1, UNK_IDX)] * sen_len
                 ctx_0_slot = [self.dict.get(ctx_0, UNK_IDX)] * sen_len
                 ctx_p1_slot = [self.dict.get(ctx_p1, UNK_IDX)] * sen_len
+                ctx_p2_slot = [self.dict.get(ctx_p2, UNK_IDX)] * sen_len
 
                 marks = mark.split()
                 mark_slot = [int(w) for w in marks]
+                
+                yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
+                      ctx_0_slot, ctx_p1_slot, ctx_p2_slot,  mark_slot
 
-                yield word_slot, predicate_slot, ctx_n1_slot, \
-                      ctx_0_slot, ctx_p1_slot, mark_slot
-
-    def predict(self, data_file):
+    def predict(self, data_file, output_file):
         """
         data_file: file name of input data.
         """
         input = self.converter(self.get_data(data_file))
         output = self.network.forwardTest(input)
-        prob = output[0]["value"]
-        lab = list(np.argsort(-prob)[:, 0])
+        lab = output[0]["id"].tolist()
 
-        with open(data_file, 'r') as fin, open('predict.res', 'w') as fout:
+        with open(data_file, 'r') as fin, open(output_file, 'w') as fout:
             index = 0
             for line in fin:
                 sen = line.split('\t')[0]
@@ -115,8 +123,8 @@ class Prediction():
 
 
 def option_parser():
-    usage = ("python predict.py -c config -w model_dir "
-             "-d word dictionary -l label_file -i input_file")
+    usage = ("python predict.py -c config -w model_dir " 
+             "-d word dictionary -l label_file -i input_file  -p pred_dict_file")
     parser = OptionParser(usage="usage: %s [options]" % usage)
     parser.add_option(
         "-c",
@@ -137,6 +145,13 @@ def option_parser():
         dest="label_file",
         default=None,
         help="label file")
+    parser.add_option(
+        "-p",
+        "--predict_dict_file",
+        action="store",
+        dest="predict_dict_file",
+        default=None,
+        help="predict_dict_file")
     parser.add_option(
         "-i",
         "--data",
@@ -150,6 +165,14 @@ def option_parser():
         dest="model_path",
         default=None,
         help="model path")
+
+    parser.add_option(
+        "-o",
+        "--output_file",
+        action="store",
+        dest="output_file",
+        default=None,
+        help="output file")
     return parser.parse_args()
 
 
@@ -160,10 +183,12 @@ def main():
     dict_file = options.dict_file
     model_path = options.model_path
     label_file = options.label_file
+    predict_dict_file = options.predict_dict_file
+    output_file = options.output_file
 
     swig_paddle.initPaddle("--use_gpu=0")
-    predict = Prediction(train_conf, dict_file, model_path, label_file)
-    predict.predict(data_file)
+    predict = Prediction(train_conf, dict_file, model_path, label_file, predict_dict_file)
+    predict.predict(data_file,output_file)
 
 
 if __name__ == '__main__':
diff --git a/demo/semantic_role_labeling/predict.sh b/demo/semantic_role_labeling/predict.sh
index a545b9a5d591b41bdbd54905cbbffc410abc8fb0..d0acdb0bd093974485475cf796c6d41ac7899135 100644
--- a/demo/semantic_role_labeling/predict.sh
+++ b/demo/semantic_role_labeling/predict.sh
@@ -26,15 +26,18 @@ LOG=`get_best_pass $log`
 LOG=(${LOG})
 best_model_path="output/pass-${LOG[1]}"
 
-
 config_file=db_lstm.py
-dict_file=./data/src.dict
-label_file=./data/tgt.dict 
+dict_file=./data/wordDict.txt
+label_file=./data/targetDict.txt 
+predicate_dict_file=./data/verbDict.txt
 input_file=./data/feature
+output_file=predict.res
  
 python predict.py \
      -c $config_file \
      -w $best_model_path \
      -l $label_file \
+     -p $predicate_dict_file  \
      -d $dict_file \
-     -i $input_file
+     -i $input_file \
+     -o $output_file
diff --git a/demo/semantic_role_labeling/train.sh b/demo/semantic_role_labeling/train.sh
index 94c7b6f31df3b5e5e059d6e1323ae0c0bec74753..b566931db0f0b977607e6ad63d2edb3b4400dfc2 100644
--- a/demo/semantic_role_labeling/train.sh
+++ b/demo/semantic_role_labeling/train.sh
@@ -16,12 +16,18 @@
 set -e
 paddle train \
   --config=./db_lstm.py \
+  --use_gpu=0 \
+  --log_period=5000 \
+  --trainer_count=1 \
+  --show_parameter_stats_period=5000 \
+  --saving_period=1 \
   --save_dir=./output \
-  --trainer_count=4 \
-  --log_period=10 \
-  --num_passes=500 \
-  --use_gpu=false \
-  --show_parameter_stats_period=10 \
-  --test_all_data_in_one_period=1 \
+  --local=1 \
+  --num_passes=10000 \
+  --test_period=0 \
+  --average_test_period=10000000 \
+  --init_model_path=./data \
+  --load_missing_parameter_strategy=rand \
+  --dot_period=100  \
 2>&1 | tee 'train.log'
 
diff --git a/doc/demo/semantic_role_labeling/semantic_role_labeling.md b/doc/demo/semantic_role_labeling/semantic_role_labeling.md
index 05fbc8278daf204df60ad19b742c920e47128c27..c6b9813f6a60c3391fbee34d83caa2b4e073ffff 100644
--- a/doc/demo/semantic_role_labeling/semantic_role_labeling.md
+++ b/doc/demo/semantic_role_labeling/semantic_role_labeling.md
@@ -30,8 +30,6 @@ Several new files appear in the `data `directory as follows.
 conll05st-release：the test data set of CoNll-2005 shared task 
 test.wsj.words：the Wall Street Journal data sentences
 test.wsj.props:  the propositional arguments
-src.dict：the dictionary of words in sentences
-tgt.dict：the labels dictionary
 feature: the extracted features from data set
 ```
 
@@ -67,6 +65,8 @@ def hook(settings, word_dict, label_dict, **kwargs):
     settings.label_dict = label_dict
     #all inputs are integral and sequential type
     settings.slots = [
+        integer_value_sequence(len(word_dict)),
+        integer_value_sequence(len(predicate_dict)),
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
         integer_value_sequence(len(word_dict)),
@@ -77,34 +77,39 @@ def hook(settings, word_dict, label_dict, **kwargs):
 ```
 The corresponding data iterator is as following:
 ```
-@provider(use_seq=True, init_hook=hook)
-def process(obj, file_name):
+@provider(init_hook=hook, should_shuffle=True, calc_batch_size=get_batch_size,
+          can_over_batch_size=False, cache=CacheType.CACHE_PASS_IN_MEM)
+def process(settings, file_name):
     with open(file_name, 'r') as fdata:
         for line in fdata:
-            sentence, predicate, ctx_n1, ctx_0, ctx_p1, mark, label = line.strip().split('\t')
+            sentence, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2,  mark, label = \
+                line.strip().split('\t')
+
             words = sentence.split()
             sen_len = len(words)
-            word_slot = [obj.word_dict.get(w, UNK_IDX) for w in words]
+            word_slot = [settings.word_dict.get(w, UNK_IDX) for w in words]
 
-            predicate_slot = [obj.word_dict.get(predicate, UNK_IDX)] * sen_len
-            ctx_n1_slot = [obj.word_dict.get(ctx_n1, UNK_IDX) ] * sen_len
-            ctx_0_slot = [obj.word_dict.get(ctx_0, UNK_IDX) ] * sen_len
-            ctx_p1_slot = [obj.word_dict.get(ctx_p1, UNK_IDX) ] * sen_len
+            predicate_slot = [settings.predicate_dict.get(predicate)] * sen_len
+            ctx_n2_slot = [settings.word_dict.get(ctx_n2, UNK_IDX)] * sen_len
+            ctx_n1_slot = [settings.word_dict.get(ctx_n1, UNK_IDX)] * sen_len
+            ctx_0_slot = [settings.word_dict.get(ctx_0, UNK_IDX)] * sen_len
+            ctx_p1_slot = [settings.word_dict.get(ctx_p1, UNK_IDX)] * sen_len
+            ctx_p2_slot = [settings.word_dict.get(ctx_p2, UNK_IDX)] * sen_len
 
             marks = mark.split()
             mark_slot = [int(w) for w in marks]
 
             label_list = label.split()
-            label_slot = [obj.label_dict.get(w) for w in label_list]
-
-            yield word_slot, predicate_slot, ctx_n1_slot, ctx_0_slot, ctx_p1_slot, mark_slot, label_slot
+            label_slot = [settings.label_dict.get(w) for w in label_list]
+            yield word_slot, predicate_slot, ctx_n2_slot, ctx_n1_slot, \
+                  ctx_0_slot, ctx_p1_slot, ctx_p2_slot, mark_slot, label_slot
 ```
-The `process`function yield 7 lists which are six features and labels.
+The `process`function yield 9 lists which are 8 features and label.
  
 ### Neural Network Config
 `db_lstm.py` is the neural network config file to load the dictionaries and define the  data provider module and network architecture during the training procedure. 
 
-Seven `data_layer` load instances from data provider. Six features are transformed into embedddings respectively, and mixed by `mixed_layer` .  Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
+Nine `data_layer` load instances from data provider. Eight features are transformed into embedddings respectively, and mixed by `mixed_layer` .  Deep bidirectional LSTM layers extract features for the softmax layer. The objective function is cross entropy of labels.
 
 ### Run Training 
 The script for training is `train.sh`, user just need to execute:
@@ -115,24 +120,36 @@ The content in `train.sh`:
 ```
 paddle train \
   --config=./db_lstm.py \
+  --use_gpu=0 \
+  --log_period=5000 \
+  --trainer_count=1 \
+  --show_parameter_stats_period=5000 \
+  --saving_period=1 \
   --save_dir=./output \
-  --trainer_count=4 \
-  --log_period=10 \
-  --num_passes=500 \
-  --use_gpu=false \
-  --show_parameter_stats_period=10 \
-  --test_all_data_in_one_period=1 \
+  --local=1 \
+  --num_passes=10000 \
+  --test_period=0 \
+  --average_test_period=10000000 \
+  --init_model_path=./data \
+  --load_missing_parameter_strategy=rand \
+  --dot_period=100  \
 2>&1 | tee 'train.log'
 ```
 
 -  \--config=./db_lstm.py : network config file.
--  \--save_di=./output: output path to save models.
--  \--trainer_count=4 : set thread number (or GPU count).
--  \--log_period=10 : print log every 20 batches.
--  \--num_passes=500: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
--  \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train.
--  \--show_parameter_stats_period=10: show parameter statistic every 100 batches.
--  \--test_all_data_in_one_period=1: test all data in every testing.
+-  \--use_gpu=false: use CPU to train, set true, if you install GPU version of PaddlePaddle and want to use GPU to train, until now crf_layer do not support GPU
+-  \--log_period=500: print log every 20 batches.
+-  \--trainer_count=1: set thread number (or GPU count).
+-  \--show_parameter_stats_period=5000: show parameter statistic every 100 batches.
+-  \--saving_period=1: save model per pass
+-  \--save_dir=./output: output path to save models.
+-  \--local=1: traing in local mode
+-  \--num_passes=10000: set pass number, one pass in PaddlePaddle means training all samples in dataset one time.
+-  \--test_period=0: run testing each pass 
+-  \--average_test_period=10000000:  do test on average parameter every average_test_period batches
+-  \--init_model_path=./data: parameter initialization path 
+-  \--load_missing_parameter_strategy=rand: random initialization unexisted parameters
+-  \--dot_period=100: print a dot per 100 batches  
 
 
 After training, the models  will be saved in directory `output`.
@@ -166,11 +183,13 @@ The script for prediction is `predict.sh`, user just need to execute:
 In `predict.sh`, user should offer the network config file, model path, label file, word dictionary file, feature file
 ```
 python predict.py 
-     -c $config_file 
-     -w $model_path 
-     -l $label_file 
-     -d $dict_file 
-     -i $input_file
+     -c $config_file \
+     -w $best_model_path \
+     -l $label_file \
+     -p $predicate_dict_file  \
+     -d $dict_file \
+     -i $input_file \
+     -o $output_file
 ```
 
 `predict.py` is the main executable python script, which includes functions: load model, load data, data prediction. The network model will output the probability distribution of labels. In the demo, we take the label with maximum probability as result. User can also implement the beam search or viterbi decoding upon the probability distribution matrix.