Add new problem: Macedonian to English (SETimes corpus)

tensor2tensor/data_generators/generator_utils.py

@@ -244,6 +244,11 @@ def gunzip_file(gz_path, new_path):
         "http://www.statmt.org/wmt13/training-parallel-un.tgz",
         ["un/undoc.2000.fr-en.en", "un/undoc.2000.fr-en.fr"]
     ],
+    # Macedonian-English
+    [
+        "https://github.com/stefan-it/nmt-mk-en/raw/master/data/setimes.mk-en.train.tgz",  # pylint: disable=line-too-long
+        ["train.mk", "train.en"]
+    ],
 ]
 
 

tensor2tensor/data_generators/problem.py

@@ -67,6 +67,8 @@ class SpaceID(object):
   ICE_TOK = 18
   # Icelandic parse tokens
   ICE_PARSE_TOK = 19
+  # Macedonian tokens
+  MK_TOK = 20
 
 
 class Problem(object):

tensor2tensor/data_generators/wmt.py

@@ -81,6 +81,31 @@ def _default_wmt_feature_encoders(data_dir, target_vocab_size):
       "targets": subtokenizer,
   }
 
+@registry.register_problem("setimes_mken_tokens_32k")
+class SETimesMkEnTokens32k(problem.Problem):
+  """Problem spec for SETimes Mk-En translation."""
+
+  @property
+  def target_vocab_size(self):
+    return 2**15  # 32768
+
+  def feature_encoders(self, data_dir):
+    return _default_wmt_feature_encoders(data_dir, self.target_vocab_size)
+
+  def generate_data(self, data_dir, tmp_dir):
+    generator_utils.generate_dataset_and_shuffle(
+        mken_wordpiece_token_generator(tmp_dir, True, self.target_vocab_size),
+        self.training_filepaths(data_dir, 100, shuffled=False),
+        mken_wordpiece_token_generator(tmp_dir, False, self.target_vocab_size),
+        self.dev_filepaths(data_dir, 1, shuffled=False))
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    vocab_size = self._encoders["inputs"].vocab_size
+    p.input_modality = {"inputs": (registry.Modalities.SYMBOL, vocab_size)}
+    p.target_modality = (registry.Modalities.SYMBOL, vocab_size)
+    p.input_space_id = problem.SpaceID.MK_TOK
+    p.target_space_id = problem.SpaceID.EN_TOK
 
 # End-of-sentence marker.
 EOS = text_encoder.EOS_TOKEN
@@ -295,6 +320,21 @@ def ende_bpe_token_generator(tmp_dir, train):
     ("dev/newsdev2017-zhen-src.zh", "dev/newsdev2017-zhen-ref.en")
 ]]
 
+# For Macedonian-English the SETimes corpus
+# from http://nlp.ffzg.hr/resources/corpora/setimes/ is used.
+# The original dataset has 207,777 parallel sentences.
+# For training the first 205,777 sentences are used.
+_MKEN_TRAIN_DATASETS = [[
+    "https://github.com/stefan-it/nmt-mk-en/raw/master/data/setimes.mk-en.train.tgz",  # pylint: disable=line-too-long
+    ("train.mk", "train.en")
+]]
+
+# For development 1000 parallel sentences are used.
+_MKEN_TEST_DATASETS = [[
+    "https://github.com/stefan-it/nmt-mk-en/raw/master/data/setimes.mk-en.dev.tgz",  # pylint: disable=line-too-long
+    ("dev.mk", "dev.en")
+]]
+
 
 def _compile_data(tmp_dir, datasets, filename):
   """Concatenate all `datasets` and save to `filename`."""
@@ -393,6 +433,19 @@ def enfr_character_generator(tmp_dir, train):
   return character_generator(data_path + ".lang1", data_path + ".lang2",
                              character_vocab, EOS)
 
+def mken_wordpiece_token_generator(tmp_dir, train, vocab_size):
+  """Wordpiece generator for the SETimes Mk-En dataset."""
+  datasets = _MKEN_TRAIN_DATASETS if train else _MKEN_TEST_DATASETS
+  source_datasets = [[item[0], [item[1][0]]] for item in datasets]
+  target_datasets = [[item[0], [item[1][1]]] for item in datasets]
+  symbolizer_vocab = generator_utils.get_or_generate_vocab(
+      tmp_dir, "tokens.vocab.%d" % vocab_size, vocab_size,
+      source_datasets + target_datasets)
+  tag = "train" if train else "dev"
+  data_path = _compile_data(tmp_dir, datasets, "setimes_mken_tok_%s" % tag)
+  return token_generator(data_path + ".lang1", data_path + ".lang2",
+                         symbolizer_vocab, EOS)
+
 
 def parsing_character_generator(tmp_dir, train):
   character_vocab = text_encoder.ByteTextEncoder()