diff --git a/fastNLP/embeddings/char_embedding.py b/fastNLP/embeddings/char_embedding.py
index 93d3ce00..114280f9 100644
--- a/fastNLP/embeddings/char_embedding.py
+++ b/fastNLP/embeddings/char_embedding.py
@@ -114,7 +114,8 @@ class CNNCharEmbedding(TokenEmbedding):
             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)
+            self.char_embedding.embedding_dim, filter_nums[i], kernel_size=kernel_sizes[i], bias=True,
+            padding=kernel_sizes[i] // 2)
             for i in range(len(kernel_sizes))])
         self._embed_size = embed_size
         self.fc = nn.Linear(sum(filter_nums), embed_size)
@@ -238,12 +239,12 @@ class LSTMCharEmbedding(TokenEmbedding):
         if pre_train_char_embed:
             self.char_embedding = StaticEmbedding(self.char_vocab, 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.fc = nn.Linear(hidden_size, embed_size)
         hidden_size = hidden_size // 2 if bidirectional else hidden_size
         
-        self.lstm = LSTM(char_emb_size, hidden_size, bidirectional=bidirectional, batch_first=True)
+        self.lstm = LSTM(self.char_embedding.embedding_dim, hidden_size, bidirectional=bidirectional, batch_first=True)
         self._embed_size = embed_size
         self.bidirectional = bidirectional
         self.requires_grad = requires_grad
diff --git a/fastNLP/embeddings/static_embedding.py b/fastNLP/embeddings/static_embedding.py
index a50ce25d..b907a278 100644
--- a/fastNLP/embeddings/static_embedding.py
+++ b/fastNLP/embeddings/static_embedding.py
@@ -68,7 +68,7 @@ class StaticEmbedding(TokenEmbedding):
         :param float word_dropout: 以多大的概率将一个词替换为unk。这样既可以训练unk也是一定的regularize。
         :param bool normalize: 是否对vector进行normalize，使得每个vector的norm为1。
         :param int min_freq: Vocabulary词频数小于这个数量的word将被指向unk。
-        :param dict kwarngs:
+        :param dict kwargs:
                 bool only_train_min_freq: 仅对train中的词语使用min_freq筛选;
                 bool only_norm_found_vector: 是否仅对在预训练中找到的词语使用normalize;
                 bool only_use_pretrain_word: 仅使用出现在pretrain词表中的词语。如果该词没有在预训练的词表中出现则为unk。如果词表
diff --git a/fastNLP/io/utils.py b/fastNLP/io/utils.py
index 496aee77..4b5230c0 100644
--- a/fastNLP/io/utils.py
+++ b/fastNLP/io/utils.py
@@ -44,14 +44,17 @@ def check_loader_paths(paths: Union[str, Dict[str, str]]) -> Dict[str, str]:
                 if 'dev' in filename:
                     if path_pair:
                         raise Exception(
-                            "File:{} in {} contains bot `{}` and `dev`.".format(filename, paths, path_pair[0]))
+                            "Directory:{} in {} contains both `{}` and `dev`.".format(filename, paths, path_pair[0]))
                     path_pair = ('dev', filename)
                 if 'test' in filename:
                     if path_pair:
                         raise Exception(
-                            "File:{} in {} contains bot `{}` and `test`.".format(filename, paths, path_pair[0]))
+                            "Directory:{} in {} contains both `{}` and `test`.".format(filename, paths, path_pair[0]))
                     path_pair = ('test', filename)
                 if path_pair:
+                    if path_pair[0] in files:
+                        raise FileExistsError(f"Two files contain `{path_pair[0]}` were found, please specify the "
+                                              f"filepath for `{path_pair[0]}`.")
                     files[path_pair[0]] = os.path.join(paths, path_pair[1])
             if 'train' not in files:
                 raise KeyError(f"There is no train file in {paths}.")