diff --git a/test/data/test_builtin_datasets.py b/test/data/test_builtin_datasets.py
index 0e2d320f42..2c8190c44a 100644
--- a/test/data/test_builtin_datasets.py
+++ b/test/data/test_builtin_datasets.py
@@ -162,6 +162,34 @@ def test_imdb(self):
         self._helper_test_func(len(test_iter), 25000, next(iter(test_iter))[1][:25], 'I love sci-fi and am will')
         del train_iter, test_iter
 
+    def test_iwslt(self):
+        from torchtext.experimental.datasets import IWSLT
+
+        train_dataset, valid_dataset, test_dataset = IWSLT()
+
+        self.assertEqual(len(train_dataset), 196884)
+        self.assertEqual(len(valid_dataset), 993)
+        self.assertEqual(len(test_dataset), 1305)
+
+        de_vocab, en_vocab = train_dataset.get_vocab()
+
+        def assert_nth_pair_is_equal(n, expected_sentence_pair):
+            de_sentence = [de_vocab.itos[index] for index in train_dataset[n][0]]
+            en_sentence = [en_vocab.itos[index] for index in train_dataset[n][1]]
+            expected_de_sentence, expected_en_sentence = expected_sentence_pair
+
+            self.assertEqual(de_sentence, expected_de_sentence)
+            self.assertEqual(en_sentence, expected_en_sentence)
+
+        assert_nth_pair_is_equal(0, (['David', 'Gallo', ':', 'Das', 'ist', 'Bill', 'Lange', '.', 'Ich', 'bin', 'Dave', 'Gallo', '.', '\n'],
+                                     ['David', 'Gallo', ':', 'This', 'is', 'Bill', 'Lange', '.', 'I', "'m", 'Dave', 'Gallo', '.', '\n']))
+        assert_nth_pair_is_equal(10, (['Die', 'meisten', 'Tiere', 'leben', 'in', 'den', 'Ozeanen', '.', '\n'],
+                                      ['Most', 'of', 'the', 'animals', 'are', 'in', 'the', 'oceans', '.', '\n']))
+        assert_nth_pair_is_equal(20, (['Es', 'ist', 'einer', 'meiner', 'Lieblinge', ',', 'weil', 'es', 'alle', 'möglichen', 'Funktionsteile', 'hat', '.', '\n'],
+                                      ['It', "'s", 'one', 'of', 'my', 'favorites', ',', 'because', 'it', "'s", 'got', 'all', 'sorts', 'of', 'working', 'parts', '.', '\n']))
+        datafile = os.path.join(self.project_root, ".data", "2016-01.tgz")
+        conditional_remove(datafile)
+
     def test_multi30k(self):
         from torchtext.experimental.datasets import Multi30k
         # smoke test to ensure multi30k works properly
diff --git a/torchtext/experimental/datasets/raw/translation.py b/torchtext/experimental/datasets/raw/translation.py
index 3b74c58421..25960bead8 100644
--- a/torchtext/experimental/datasets/raw/translation.py
+++ b/torchtext/experimental/datasets/raw/translation.py
@@ -62,7 +62,7 @@
     'WMT14':
     'https://drive.google.com/uc?export=download&id=0B_bZck-ksdkpM25jRUN2X2UxMm8',
     'IWSLT':
-    'https://wit3.fbk.eu/archive/2016-01//texts/{}/{}/{}.tgz'
+    'https://drive.google.com/uc?id=1l5y6Giag9aRPwGtuZHswh3w5v3qEz8D8'
 }
 
 
@@ -125,14 +125,26 @@ def _setup_datasets(dataset_name,
     src_eval, tgt_eval = valid_filenames
     src_test, tgt_test = test_filenames
 
-    extracted_files = []
+    extracted_files = []  # list of paths to the extracted files
     if isinstance(URLS[dataset_name], list):
         for idx, f in enumerate(URLS[dataset_name]):
-            dataset_tar = download_from_url(f, root=root, hash_value=MD5[dataset_name][idx], hash_type='md5')
+            dataset_tar = download_from_url(
+                f, root=root, hash_value=MD5[dataset_name][idx], hash_type='md5')
             extracted_files.extend(extract_archive(dataset_tar))
     elif isinstance(URLS[dataset_name], str):
         dataset_tar = download_from_url(URLS[dataset_name], root=root, hash_value=MD5[dataset_name], hash_type='md5')
-        extracted_files.extend(extract_archive(dataset_tar))
+        extracted_dataset_tar = extract_archive(dataset_tar)
+        if dataset_name == 'IWSLT':
+            # IWSLT dataset's url downloads a multilingual tgz.
+            # We need to take an extra step to pick out the specific language pair from it.
+            src_language = train_filenames[0].split(".")[-1]
+            tgt_language = train_filenames[1].split(".")[-1]
+            languages = "-".join([src_language, tgt_language])
+            iwslt_tar = '.data/2016-01/texts/{}/{}/{}.tgz'
+            iwslt_tar = iwslt_tar.format(
+                src_language, tgt_language, languages)
+            extracted_dataset_tar = extract_archive(iwslt_tar)
+        extracted_files.extend(extracted_dataset_tar)
     else:
         raise ValueError(
             "URLS for {} has to be in a form or list or string".format(
@@ -418,10 +430,6 @@ def IWSLT(train_filenames=('train.de-en.de', 'train.de-en.en'),
         >>> from torchtext.experimental.datasets.raw import IWSLT
         >>> train_dataset, valid_dataset, test_dataset = IWSLT()
     """
-    src_language = train_filenames[0].split(".")[-1]
-    tgt_language = train_filenames[1].split(".")[-1]
-    languages = "-".join([src_language, tgt_language])
-    URLS["IWSLT"] = URLS["IWSLT"].format(src_language, tgt_language, languages)
     return _setup_datasets("IWSLT", train_filenames, valid_filenames, test_filenames, data_select, root)
 
 
@@ -567,6 +575,6 @@ def WMT14(train_filenames=('train.tok.clean.bpe.32000.de',
                  'acb5ea26a577ceccfae6337181c31716',
                  '873a377a348713d3ab84db1fb57cdede',
                  '680816e0938fea5cf5331444bc09a4cf'],
-    'IWSLT': '6ff9ab8ea16fb352597c2784e0391fa8',
+    'IWSLT': 'c393ed3fc2a1b0f004b3331043f615ae',
     'WMT14': '874ab6bbfe9c21ec987ed1b9347f95ec'
 }