diff --git a/bin/parser/train.py b/bin/parser/train.py
index 747bf978d..89d9b1f4c 100755
--- a/bin/parser/train.py
+++ b/bin/parser/train.py
@@ -61,13 +61,8 @@ def read_docparse_gold(file_):
         tags = []
         ids = []
         lines = sent_str.strip().split('\n')
-<<<<<<< HEAD
         raw_text = lines.pop(0).strip()
         tok_text = lines.pop(0).strip()
-=======
-        raw_text = lines.pop(0)
-        tok_text = lines.pop(0)
->>>>>>> master
         for i, line in enumerate(lines):
             id_, word, pos_string, head_idx, label = _parse_line(line)
             if label == 'root':
@@ -200,9 +195,9 @@ def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
     setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
                     pos_model_dir)
 
-    left_labels, right_labels = get_labels(paragraphs)
+    labels = Language.ParserTransitionSystem.get_labels(gold_sents)
     Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
-                 left_labels=left_labels, right_labels=right_labels)
+                 labels=labels)
 
     nlp = Language()
     
@@ -210,14 +205,12 @@ def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
         heads_corr = 0
         pos_corr = 0
         n_tokens = 0
-        for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
-                                                         gold_preproc=gold_preproc):
+        for gold_sent in gold_sents:
+            tokens = nlp.tokenizer(gold_sent.raw)
+            gold_sent.align_to_tokens(tokens)
             nlp.tagger(tokens)
-            try:
-                heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=force_gold)
-            except OracleError:
-                continue
-            pos_corr += nlp.tagger.train(tokens, tag_strs)
+            heads_corr += nlp.parser.train(tokens, gold_sent, force_gold=force_gold)
+            pos_corr += nlp.tagger.train(tokens, gold_parse.tags)
             n_tokens += len(tokens)
         acc = float(heads_corr) / n_tokens
         pos_acc = float(pos_corr) / n_tokens
@@ -265,10 +258,9 @@ def evaluate(Language, dev_loc, model_dir, gold_preproc=False):
 
 
 def main(train_loc, dev_loc, model_dir):
-    with codecs.open(train_loc, 'r', 'utf8') as file_:
-        train_sents  = read_docparse_gold(file_)
-    train(English, train_sents, model_dir, gold_preproc=False, force_gold=False)
-    print evaluate(English, dev_loc, model_dir, gold_preproc=False)
+    train(English, read_docparse_gold(train_loc), model_dir,
+          gold_preproc=False, force_gold=False)
+    print evaluate(English, read_docparse_gold(dev_loc), model_dir, gold_preproc=False)
     
 
 if __name__ == '__main__':