diff --git a/spacy/train.py b/spacy/train.py
index 097218310..a86569100 100644
--- a/spacy/train.py
+++ b/spacy/train.py
@@ -14,22 +14,31 @@ class Trainer(object):
         self.gold_tuples = gold_tuples
 
     def epochs(self, nr_epoch, augment_data=None, gold_preproc=False):
-        def _epoch():
-            for raw_text, paragraph_tuples in self.gold_tuples:
+        cached_golds = {}
+        def _epoch(indices):
+            for i in indices:
+                raw_text, paragraph_tuples = self.gold_tuples[i]
                 if gold_preproc:
                     raw_text = None
                 else:
                     paragraph_tuples = merge_sents(paragraph_tuples)
-                if augment_data is not None:
+                if augment_data is None:
+                    docs = self.make_docs(raw_text, paragraph_tuples)
+                    if i in cached_golds:
+                        golds = cached_golds[i]
+                    else:
+                        golds = self.make_golds(docs, paragraph_tuples)
+                else:
                     raw_text, paragraph_tuples = augment_data(raw_text, paragraph_tuples)
-                docs = self.make_docs(raw_text, paragraph_tuples)
-                golds = self.make_golds(docs, paragraph_tuples)
+                    docs = self.make_docs(raw_text, paragraph_tuples)
+                    golds = self.make_golds(docs, paragraph_tuples)
                 for doc, gold in zip(docs, golds):
                     yield doc, gold
 
+        indices = list(range(len(self.gold_tuples)))
         for itn in range(nr_epoch):
-            random.shuffle(self.gold_tuples)
-            yield _epoch()
+            random.shuffle(indices)
+            yield _epoch(indices)
  
     def update(self, doc, gold):
         for process in self.nlp.pipeline:
@@ -48,7 +57,7 @@ class Trainer(object):
             docs = self.make_docs(raw_text, paragraph_tuples)
             golds = self.make_golds(docs, paragraph_tuples)
             for doc, gold in zip(docs, golds):
-                for process in self.nlp.pipeline[1:]:
+                for process in self.nlp.pipeline:
                     process(doc)
                 scorer.score(doc, gold)
         return scorer
@@ -62,8 +71,8 @@ class Trainer(object):
 
     def make_golds(self, docs, paragraph_tuples):
         if len(docs) == 1:
-            return [GoldParse(docs[0], sent_tuples[0])
+            return [GoldParse.from_annot_tuples(docs[0], sent_tuples[0])
                     for sent_tuples in paragraph_tuples]
         else:
-            return [GoldParse(doc, sent_tuples[0])
+            return [GoldParse.from_annot_tuples(doc, sent_tuples[0])
                     for doc, sent_tuples in zip(docs, paragraph_tuples)]