Fix the EnZh task

tensor2tensor/data_generators/translate_enzh.py

@@ -35,21 +35,23 @@
 
 # End-of-sentence marker.
 EOS = text_encoder.EOS_ID
-
-_ZHEN_TRAIN_DATASETS = [[("http://data.statmt.org/wmt17/translation-task/"
+# This is far from being the real WMT17 task - only toyset here
+# you need to register to get UN data and CWT data
+# also by convention this is EN to ZH - use translate_enzh_wmt8k_rev for ZH to EN task
+_ENZH_TRAIN_DATASETS = [[("http://data.statmt.org/wmt17/translation-task/"
                           "training-parallel-nc-v12.tgz"),
-                         ("training/news-commentary-v12.zh-en.zh",
-                          "training/news-commentary-v12.zh-en.en")]]
+                         ("training/news-commentary-v12.zh-en.en",
+                          "training/news-commentary-v12.zh-en.zh")]]
 
-_ZHEN_TEST_DATASETS = [[
+_ENZH_TEST_DATASETS = [[
     "http://data.statmt.org/wmt17/translation-task/dev.tgz",
-    ("dev/newsdev2017-zhen-src.zh.sgm", "dev/newsdev2017-zhen-ref.en.sgm")
+    ("dev/newsdev2017-zhen-src.en.sgm", "dev/newsdev2017-zhen-ref.zh.sgm")
 ]]
 
 
 @registry.register_problem
 class TranslateEnzhWmt8k(translate.TranslateProblem):
-  """Problem spec for WMT Zh-En translation."""
+  """Problem spec for WMT En-Zh translation."""
 
   @property
   def targeted_vocab_size(self):
@@ -61,16 +63,16 @@ def num_shards(self):
 
   @property
   def source_vocab_name(self):
-    return "vocab.zhen-zh.%d" % self.targeted_vocab_size
+    return "vocab.enzh-en.%d" % self.targeted_vocab_size
 
   @property
   def target_vocab_name(self):
-    return "vocab.zhen-en.%d" % self.targeted_vocab_size
+    return "vocab.enzh-zh.%d" % self.targeted_vocab_size
 
   def generator(self, data_dir, tmp_dir, train):
-    datasets = _ZHEN_TRAIN_DATASETS if train else _ZHEN_TEST_DATASETS
-    source_datasets = [[item[0], [item[1][0]]] for item in _ZHEN_TRAIN_DATASETS]
-    target_datasets = [[item[0], [item[1][1]]] for item in _ZHEN_TRAIN_DATASETS]
+    datasets = _ENZH_TRAIN_DATASETS if train else _ENZH_TEST_DATASETS
+    source_datasets = [[item[0], [item[1][0]]] for item in _ENZH_TRAIN_DATASETS]
+    target_datasets = [[item[0], [item[1][1]]] for item in _ENZH_TRAIN_DATASETS]
     source_vocab = generator_utils.get_or_generate_vocab(
         data_dir, tmp_dir, self.source_vocab_name, self.targeted_vocab_size,
         source_datasets)
@@ -79,21 +81,18 @@ def generator(self, data_dir, tmp_dir, train):
         target_datasets)
     tag = "train" if train else "dev"
     data_path = translate.compile_data(tmp_dir, datasets,
-                                       "wmt_zhen_tok_%s" % tag)
-    # We generate English->X data by convention, to train reverse translation
-    # just add the "_rev" suffix to the problem name, e.g., like this.
-    #   --problems=translate_enzh_wmt8k_rev
-    return translate.bi_vocabs_token_generator(data_path + ".lang2",
-                                               data_path + ".lang1",
+                                       "wmt_enzh_tok_%s" % tag)
+    return translate.bi_vocabs_token_generator(data_path + ".lang1",
+                                               data_path + ".lang2",
                                                source_vocab, target_vocab, EOS)
 
   @property
   def input_space_id(self):
-    return problem.SpaceID.ZH_TOK
+    return problem.SpaceID.EN_TOK
 
   @property
   def target_space_id(self):
-    return problem.SpaceID.EN_TOK
+    return problem.SpaceID.ZH_TOK
 
   def feature_encoders(self, data_dir):
     source_vocab_filename = os.path.join(data_dir, self.source_vocab_name)