diff --git a/fastNLP/embeddings/char_embedding.py b/fastNLP/embeddings/char_embedding.py
index e772703a..520e85e6 100644
--- a/fastNLP/embeddings/char_embedding.py
+++ b/fastNLP/embeddings/char_embedding.py
@@ -14,7 +14,7 @@ from ..modules.encoder.lstm import LSTM
 from ..core.vocabulary import Vocabulary
 from .embedding import TokenEmbedding
 from .utils import _construct_char_vocab_from_vocab
-
+from .utils import get_embeddings
 
 class CNNCharEmbedding(TokenEmbedding):
     """
@@ -50,7 +50,7 @@ class CNNCharEmbedding(TokenEmbedding):
         没有的话将自动下载。如果输入为None则使用embedding_dim的维度随机初始化一个embedding.
     """
     def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0,
-                 dropout:float=0.5, filter_nums: List[int]=(40, 30, 20), kernel_sizes: List[int]=(5, 3, 1),
+                 dropout:float=0, filter_nums: List[int]=(40, 30, 20), kernel_sizes: List[int]=(5, 3, 1),
                  pool_method: str='max', activation='relu', min_char_freq: int=2, pre_train_char_embed: str=None):
         super(CNNCharEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout)
 
@@ -58,7 +58,6 @@ class CNNCharEmbedding(TokenEmbedding):
             assert kernel % 2 == 1, "Only odd kernel is allowed."
 
         assert pool_method in ('max', 'avg')
-        self.dropout = nn.Dropout(dropout)
         self.pool_method = pool_method
         # activation function
         if isinstance(activation, str):
@@ -96,7 +95,7 @@ class CNNCharEmbedding(TokenEmbedding):
         if pre_train_char_embed:
             self.char_embedding = StaticEmbedding(self.char_vocab, model_dir_or_name=pre_train_char_embed)
         else:
-            self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
+            self.char_embedding = get_embeddings((len(self.char_vocab), char_emb_size))
 
         self.convs = nn.ModuleList([nn.Conv1d(
             char_emb_size, filter_nums[i], kernel_size=kernel_sizes[i], bias=True, padding=kernel_sizes[i] // 2)
@@ -164,6 +163,8 @@ class CNNCharEmbedding(TokenEmbedding):
         for name, param in self.named_parameters():
             if 'words_to_chars_embedding' in name or 'word_lengths' in name:  # 这个不能reset
                 continue
+            if 'char_embedding' in name:
+                continue
             if param.data.dim()>1:
                 nn.init.xavier_uniform_(param, 1)
             else:
@@ -203,15 +204,14 @@ class LSTMCharEmbedding(TokenEmbedding):
         没有的话将自动下载。如果输入为None则使用embedding_dim的维度随机初始化一个embedding.
     """
     def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0,
-                 dropout:float=0.5, hidden_size=50,pool_method: str='max', activation='relu', min_char_freq: int=2,
+                 dropout:float=0, hidden_size=50,pool_method: str='max', activation='relu', min_char_freq: int=2,
                  bidirectional=True, pre_train_char_embed: str=None):
-        super(LSTMCharEmbedding, self).__init__(vocab)
+        super(LSTMCharEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout)
 
         assert hidden_size % 2 == 0, "Only even kernel is allowed."
 
         assert pool_method in ('max', 'avg')
         self.pool_method = pool_method
-        self.dropout = nn.Dropout(dropout)
         # activation function
         if isinstance(activation, str):
             if activation.lower() == 'relu':