diff --git a/tensorflow/python/keras/layers/wrappers.py b/tensorflow/python/keras/layers/wrappers.py
index a7510f0d1f1..01b801330df 100644
--- a/tensorflow/python/keras/layers/wrappers.py
+++ b/tensorflow/python/keras/layers/wrappers.py
@@ -418,8 +418,8 @@ class Bidirectional(Wrapper):
                        'Merge mode should be one of '
                        '{"sum", "mul", "ave", "concat", None}')
     if getattr(layer, 'zero_output_for_mask', None) is not None:
-      # Force the zero_output_for_mask to be True if it presents.
-      layer.zero_output_for_mask = True
+      # Force the zero_output_for_mask to be True if returning sequences.
+      layer.zero_output_for_mask = layer.return_sequences
 
     self.forward_layer = copy.copy(layer)
     config = layer.get_config()
diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py
index fec89382063..78f95b31f13 100644
--- a/tensorflow/python/keras/layers/wrappers_test.py
+++ b/tensorflow/python/keras/layers/wrappers_test.py
@@ -688,7 +688,33 @@ class BidirectionalTest(test.TestCase):
       y_np_3 = model.predict([x_np, s_fw_np, s_bk_np, c_np])
       self.assertAllClose(y_np, y_np_3, atol=1e-4)
 
-  def test_Bidirectional_with_masking(self):
+  def test_Bidirectional_last_output_with_masking(self):
+    rnn = keras.layers.LSTM
+    samples = 2
+    dim = 5
+    timesteps = 3
+    units = 3
+    merge_mode = 'concat'
+    x = np.random.rand(samples, timesteps, dim)
+    # clear the first record's timestep 2. Last output should be same as state,
+    # not zeroed.
+    x[0, 2] = 0
+
+    with self.cached_session():
+      inputs = keras.Input((timesteps, dim))
+      masked_inputs = keras.layers.Masking()(inputs)
+      wrapped = keras.layers.Bidirectional(
+          rnn(units, return_state=True), merge_mode=merge_mode)
+      outputs = _to_list(wrapped(masked_inputs, training=True))
+      self.assertEqual(len(outputs), 5)
+      self.assertEqual(outputs[0].get_shape().as_list(), [None, units * 2])
+
+      model = keras.Model(inputs, outputs)
+      y = _to_list(model.predict(x))
+      self.assertEqual(len(y), 5)
+      self.assertAllClose(y[0], np.concatenate([y[1], y[3]], axis=1))
+
+  def test_Bidirectional_sequence_output_with_masking(self):
     rnn = keras.layers.LSTM
     samples = 2
     dim = 5