diff --git a/tensorflow/contrib/seq2seq/python/ops/helper.py b/tensorflow/contrib/seq2seq/python/ops/helper.py
index 9d3f8ad4411..a716dcba738 100644
--- a/tensorflow/contrib/seq2seq/python/ops/helper.py
+++ b/tensorflow/contrib/seq2seq/python/ops/helper.py
@@ -520,7 +520,8 @@ class SampleEmbeddingHelper(GreedyEmbeddingHelper):
   result through an embedding layer to get the next input.
   """
 
-  def __init__(self, embedding, start_tokens, end_token, seed=None):
+  def __init__(self, embedding, start_tokens, end_token,
+               softmax_temperature=None, seed=None):
     """Initializer.
 
     Args:
@@ -529,7 +530,12 @@ class SampleEmbeddingHelper(GreedyEmbeddingHelper):
         will be passed to the decoder input.
       start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
       end_token: `int32` scalar, the token that marks end of decoding.
-      seed: The sampling seed.
+      softmax_temperature: (Optional) `float32` scalar, value to divide the
+        logits by before computing the softmax. Larger values (above 1.0) result
+        in more random samples, while smaller values push the sampling
+        distribution towards the argmax. Must be strictly greater than 0.
+        Defaults to 1.0.
+      seed: (Optional) The sampling seed.
 
     Raises:
       ValueError: if `start_tokens` is not a 1D tensor or `end_token` is not a
@@ -537,6 +543,7 @@ class SampleEmbeddingHelper(GreedyEmbeddingHelper):
     """
     super(SampleEmbeddingHelper, self).__init__(
         embedding, start_tokens, end_token)
+    self._softmax_temperature = softmax_temperature
     self._seed = seed
 
   def sample(self, time, outputs, state, name=None):
@@ -546,7 +553,12 @@ class SampleEmbeddingHelper(GreedyEmbeddingHelper):
     if not isinstance(outputs, ops.Tensor):
       raise TypeError("Expected outputs to be a single Tensor, got: %s" %
                       type(outputs))
-    sample_id_sampler = categorical.Categorical(logits=outputs)
+    if self._softmax_temperature is None:
+      logits = outputs
+    else:
+      logits = outputs / self._softmax_temperature
+
+    sample_id_sampler = categorical.Categorical(logits=logits)
     sample_ids = sample_id_sampler.sample(seed=self._seed)
 
     return sample_ids