diff --git a/allennlp/data/fields/array_field.py b/allennlp/data/fields/array_field.py
index d888d542781..93e677e05cf 100644
--- a/allennlp/data/fields/array_field.py
+++ b/allennlp/data/fields/array_field.py
@@ -27,7 +27,8 @@ def as_tensor(self, padding_lengths: Dict[str, int]) -> torch.Tensor:
         max_shape = [padding_lengths["dimension_{}".format(i)]
                      for i in range(len(padding_lengths))]
 
-        return_array = numpy.ones(max_shape, "float32") * self.padding_value
+        # Convert explicitly to an ndarray just in case it's an scalar (it'd end up not being an ndarray otherwise)
+        return_array = numpy.asarray(numpy.ones(max_shape, "float32") * self.padding_value)
 
         # If the tensor has a different shape from the largest tensor, pad dimensions with zeros to
         # form the right shaped list of slices for insertion into the final tensor.
diff --git a/allennlp/tests/data/fields/array_field_test.py b/allennlp/tests/data/fields/array_field_test.py
index 63abad1e3c0..cad198575bb 100644
--- a/allennlp/tests/data/fields/array_field_test.py
+++ b/allennlp/tests/data/fields/array_field_test.py
@@ -57,3 +57,9 @@ def test_padding_handles_list_fields_with_padding_values(self):
     def test_printing_doesnt_crash(self):
         array = ArrayField(numpy.ones([2, 3]), padding_value=-1)
         print(array)
+
+    def test_as_tensor_works_with_scalar(self):
+        array = ArrayField(numpy.asarray(42))
+        returned_tensor = array.as_tensor(array.get_padding_lengths())
+        current_tensor = numpy.asarray(42)
+        numpy.testing.assert_array_equal(returned_tensor, current_tensor)