diff --git a/src/gluonnlp/__init__.py b/src/gluonnlp/__init__.py
index f211dba014..f3d457b97b 100644
--- a/src/gluonnlp/__init__.py
+++ b/src/gluonnlp/__init__.py
@@ -31,7 +31,7 @@
 from . import initializer
 from .vocab import Vocab
 
-__version__ = '0.8.2'
+__version__ = '0.8.3'
 
 __all__ = ['data',
            'model',
diff --git a/src/gluonnlp/model/sampled_block.py b/src/gluonnlp/model/sampled_block.py
index 99e4d97cd3..695c1a46bb 100644
--- a/src/gluonnlp/model/sampled_block.py
+++ b/src/gluonnlp/model/sampled_block.py
@@ -70,15 +70,15 @@ def hybrid_forward(self, F, x, sampled_values, label, w_all, b_all):
 
         # remove accidental hits
         if self._remove_accidental_hits:
-            label_vec = F.reshape(label, (-1, 1))
-            sample_vec = F.reshape(sampled_candidates, (1, -1))
-            mask = F.broadcast_equal(label_vec, sample_vec) * -1e37
+            label_vec = F.reshape(label, (-1, 1)).astype('int32')
+            sample_vec = F.reshape(sampled_candidates, (1, -1)).astype('int32')
+            mask = F.broadcast_equal(label_vec, sample_vec).astype('float32') * -1e37
             pred_sampled = pred_sampled + mask
 
         # subtract log(q)
-        expected_count_sampled = F.reshape(expected_count_sampled,
-                                           shape=(1, self._num_sampled))
-        expected_count_true = expected_count_true.reshape((-1,))
+        expected_count_sampled = expected_count_sampled.astype('float32')
+        expected_count_sampled = expected_count_sampled.reshape(shape=(1, self._num_sampled))
+        expected_count_true = expected_count_true.astype('float32').reshape((-1,))
         pred_true = pred_true - F.log(expected_count_true)
         pred_true = pred_true.reshape((-1, 1))
         pred_sampled = F.broadcast_sub(pred_sampled, F.log(expected_count_sampled))
@@ -174,7 +174,7 @@ def hybrid_forward(self, F, x, sampled_values, label, weight, bias):
         # (batch_size,)
         label = F.reshape(label, shape=(-1,))
         # (num_sampled+batch_size,)
-        ids = F.concat(sampled_candidates, label, dim=0)
+        ids = F.concat(sampled_candidates.astype('int32'), label.astype('int32'), dim=0)
         # lookup weights and biases
         # (num_sampled+batch_size, dim)
         w_all = F.Embedding(data=ids, weight=weight,
@@ -477,7 +477,7 @@ def forward(self, x, sampled_values, label): # pylint: disable=arguments-differ
         # (batch_size,)
         label = label.reshape(shape=(-1,))
         # (num_sampled+batch_size,)
-        ids = nd.concat(sampled_candidates, label, dim=0)
+        ids = nd.concat(sampled_candidates.astype('int32'), label.astype('int32'), dim=0)
         # lookup weights and biases
         weight = self.weight.row_sparse_data(ids)
         bias = self.bias.data(ids.context)
diff --git a/tests/unittest/test_sampled_logits.py b/tests/unittest/test_sampled_logits.py
index 802f1e6a4b..a92f21bae1 100644
--- a/tests/unittest/test_sampled_logits.py
+++ b/tests/unittest/test_sampled_logits.py
@@ -27,7 +27,9 @@
 import pytest
 
 @pytest.mark.parametrize('f', [nlp.model.NCEDense, nlp.model.SparseNCEDense])
-def test_nce_loss(f):
+@pytest.mark.parametrize('cls_dtype', ['float32', 'int32'])
+@pytest.mark.parametrize('count_dtype', ['float32', 'int32'])
+def test_nce_loss(f, cls_dtype, count_dtype):
     ctx = mx.cpu()
     batch_size = 2
     num_sampled = 3
@@ -40,9 +42,9 @@ def test_nce_loss(f):
     trainer = mx.gluon.Trainer(model.collect_params(), 'sgd')
     x = mx.nd.ones((batch_size, num_hidden))
     y = mx.nd.ones((batch_size,))
-    sampled_cls = mx.nd.ones((num_sampled,))
-    sampled_cls_cnt = mx.nd.ones((num_sampled,))
-    true_cls_cnt = mx.nd.ones((batch_size,))
+    sampled_cls = mx.nd.ones((num_sampled,), dtype=cls_dtype)
+    sampled_cls_cnt = mx.nd.ones((num_sampled,), dtype=count_dtype)
+    true_cls_cnt = mx.nd.ones((batch_size,), dtype=count_dtype)
     samples = (sampled_cls, sampled_cls_cnt, true_cls_cnt)
     with mx.autograd.record():
         pred, new_y = model(x, samples, y)
@@ -53,7 +55,9 @@ def test_nce_loss(f):
     mx.nd.waitall()
 
 @pytest.mark.parametrize('f', [nlp.model.ISDense, nlp.model.SparseISDense])
-def test_is_softmax_loss(f):
+@pytest.mark.parametrize('cls_dtype', ['float32', 'int32'])
+@pytest.mark.parametrize('count_dtype', ['float32', 'int32'])
+def test_is_softmax_loss(f, cls_dtype, count_dtype):
     ctx = mx.cpu()
     batch_size = 2
     num_sampled = 3
@@ -66,9 +70,9 @@ def test_is_softmax_loss(f):
     trainer = mx.gluon.Trainer(model.collect_params(), 'sgd')
     x = mx.nd.ones((batch_size, num_hidden))
     y = mx.nd.ones((batch_size,))
-    sampled_cls = mx.nd.ones((num_sampled,))
-    sampled_cls_cnt = mx.nd.ones((num_sampled,))
-    true_cls_cnt = mx.nd.ones((batch_size,))
+    sampled_cls = mx.nd.ones((num_sampled,), dtype=cls_dtype)
+    sampled_cls_cnt = mx.nd.ones((num_sampled,), dtype=count_dtype)
+    true_cls_cnt = mx.nd.ones((batch_size,), dtype=count_dtype)
     samples = (sampled_cls, sampled_cls_cnt, true_cls_cnt)
     with mx.autograd.record():
         pred, new_y = model(x, samples, y)