diff --git a/fastNLP/embeddings/static_embedding.py b/fastNLP/embeddings/static_embedding.py
index c3fe7966..15cb05f6 100644
--- a/fastNLP/embeddings/static_embedding.py
+++ b/fastNLP/embeddings/static_embedding.py
@@ -122,6 +122,7 @@ class StaticEmbedding(TokenEmbedding):
                 unknown_idx = lowered_vocab.unknown_idx
             else:
                 unknown_idx = embedding.size(0) - 1  # 否则是最后一个为unknow
+                self.words_to_words = nn.Parameter(torch.arange(len(vocab)).long(), requires_grad=False)
             words_to_words = nn.Parameter(torch.full((len(vocab),), fill_value=unknown_idx).long(),
                                           requires_grad=False)
             for word, index in vocab:
@@ -129,7 +130,7 @@ class StaticEmbedding(TokenEmbedding):
                     word = word.lower()
                     if word not in lowered_vocab and lowered_vocab._is_word_no_create_entry(word):
                         continue  # 如果不需要创建entry,已经默认unknown了
-                words_to_words[index] = words_to_words[lowered_vocab.to_index(word)]
+                words_to_words[index] = self.words_to_words[lowered_vocab.to_index(word)]
             self.words_to_words = words_to_words
             self._word_unk_index = lowered_vocab.unknown_idx  # 替换一下unknown的index
         else:
@@ -137,6 +138,7 @@ class StaticEmbedding(TokenEmbedding):
                 embedding = self._load_with_vocab(model_path, vocab=vocab, init_method=init_method)
             else:
                 embedding = self._randomly_init_embed(len(vocab), embedding_dim, init_method)
+                self.words_to_words = nn.Parameter(torch.arange(len(vocab)).long(), requires_grad=False)
         if normalize:
             embedding /= (torch.norm(embedding, dim=1, keepdim=True) + 1e-12)