diff --git a/spacy/cli/ud_train.py b/spacy/cli/ud_train.py
index 86fdabca4..6e00f3193 100644
--- a/spacy/cli/ud_train.py
+++ b/spacy/cli/ud_train.py
@@ -161,9 +161,19 @@ def golds_to_gold_tuples(docs, golds):
 ##############
 
 def evaluate(nlp, text_loc, gold_loc, sys_loc, limit=None):
-    with text_loc.open('r', encoding='utf8') as text_file:
-        texts = split_text(text_file.read())
-        docs = list(nlp.pipe(texts))
+    if text_loc.parts[-1].endswith('.conllu'):
+        docs = []
+        with text_loc.open() as file_:
+            for conllu_doc in read_conllu(file_):
+                for conllu_sent in conllu_doc:
+                    words = [line[1] for line in conllu_sent]
+                    docs.append(Doc(nlp.vocab, words=words))
+        for name, component in nlp.pipeline:
+            docs = list(component.pipe(docs))
+    else:
+        with text_loc.open('r', encoding='utf8') as text_file:
+            texts = split_text(text_file.read())
+            docs = list(nlp.pipe(texts))
     with sys_loc.open('w', encoding='utf8') as out_file:
         write_conllu(docs, out_file)
     with gold_loc.open('r', encoding='utf8') as gold_file:
@@ -261,12 +271,12 @@ def load_nlp(corpus, config, vectors=None):
                                                                             
 
 def initialize_pipeline(nlp, docs, golds, config, device):
+    nlp.add_pipe(nlp.create_pipe('tagger'))
     nlp.add_pipe(nlp.create_pipe('parser'))
     if config.multitask_tag:
         nlp.parser.add_multitask_objective('tag')
     if config.multitask_sent:
         nlp.parser.add_multitask_objective('sent_start')
-    nlp.add_pipe(nlp.create_pipe('tagger'))
     for gold in golds:
         for tag in gold.tags:
             if tag is not None:
@@ -328,10 +338,12 @@ class TreebankPaths(object):
     config=("Path to json formatted config file", "positional"),
     limit=("Size limit", "option", "n", int),
     use_gpu=("Use GPU", "option", "g", int),
+    use_oracle_segments=("Use oracle segments", "flag", "G", int),
     vectors_dir=("Path to directory with pre-trained vectors, named e.g. en/",
                          "option", "v", Path),
 )
-def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None):
+def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None,
+        use_oracle_segments=False):
     spacy.util.fix_random_seed()
     lang.zh.Chinese.Defaults.use_jieba = False
     lang.ja.Japanese.Defaults.use_janome = False
@@ -344,13 +356,17 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=No
     nlp = load_nlp(paths.lang, config, vectors=vectors_dir)
 
     docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
-                            max_doc_length=config.max_doc_length, limit=limit)
+                                        max_doc_length=None, limit=limit)
 
     optimizer = initialize_pipeline(nlp, docs, golds, config, use_gpu)
 
     batch_sizes = compounding(config.batch_size//10, config.batch_size, 1.001)
+    nlp.parser.cfg['beam_update_prob'] = 1.0
     for i in range(config.nr_epoch):
-        docs = [nlp.make_doc(doc.text) for doc in docs]
+        docs, golds = read_data(nlp, paths.train.conllu.open(), paths.train.text.open(),
+                                max_doc_length=config.max_doc_length, limit=limit,
+                                oracle_segments=use_oracle_segments,
+                                raw_text=not use_oracle_segments)
         Xs = list(zip(docs, golds))
         random.shuffle(Xs)
         batches = minibatch_by_words(Xs, size=batch_sizes)
@@ -365,7 +381,12 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=No
         
         out_path = parses_dir / corpus / 'epoch-{i}.conllu'.format(i=i)
         with nlp.use_params(optimizer.averages):
-            parsed_docs, scores = evaluate(nlp, paths.dev.text, paths.dev.conllu, out_path)
+            if use_oracle_segments:
+                parsed_docs, scores = evaluate(nlp, paths.dev.conllu,
+                                               paths.dev.conllu, out_path)
+            else:
+                parsed_docs, scores = evaluate(nlp, paths.dev.text,
+                                               paths.dev.conllu, out_path)
             print_progress(i, losses, scores)
             _render_parses(i, parsed_docs[:50])