diff --git a/modelscope/metrics/text_generation_metric.py b/modelscope/metrics/text_generation_metric.py
index 9bca7cf3..c2d9c6a8 100644
--- a/modelscope/metrics/text_generation_metric.py
+++ b/modelscope/metrics/text_generation_metric.py
@@ -2,7 +2,7 @@
 
 from typing import Dict, Iterable, List
 
-from nltk.translate.bleu_score import sentence_bleu
+from nltk.translate.bleu_score import SmoothingFunction, corpus_bleu
 from rouge import Rouge
 
 from modelscope.metainfo import Metrics
@@ -63,14 +63,18 @@ class TextGenerationMetric(Metric):
         rouge_scores = self.rouge.get_scores(hyps=preds, refs=tgts)
         rouge_1 = mean(map(lambda score: score['rouge-1']['f'], rouge_scores))
         rouge_l = mean(map(lambda score: score['rouge-l']['f'], rouge_scores))
-        pred_split = tuple(pred.split(' ') for pred in self.preds)
-        tgt_split = tuple(tgt.split(' ') for tgt in self.tgts)
-        bleu_1 = mean(
-            sentence_bleu([tgt], pred, weights=(1, 0, 0, 0))
-            for pred, tgt in zip(pred_split, tgt_split))
-        bleu_4 = mean(
-            sentence_bleu([tgt], pred)
-            for pred, tgt in zip(pred_split, tgt_split))
+
+        pred_list = [each.strip().split(' ') for each in self.preds]
+        tgt_list = [[each.strip().split(' ')] for each in self.tgts]
+        bleu_1 = corpus_bleu(
+            tgt_list,
+            pred_list,
+            weights=(1, 0, 0, 0),
+            smoothing_function=SmoothingFunction().method3)
+        bleu_4 = corpus_bleu(
+            tgt_list,
+            pred_list,
+            smoothing_function=SmoothingFunction().method3)
         return {
             MetricKeys.ROUGE_1: rouge_1,
             MetricKeys.ROUGE_L: rouge_l,