diff --git a/spacy/cli/ud_train.py b/spacy/cli/ud_train.py
index 853cff9b3..86fdabca4 100644
--- a/spacy/cli/ud_train.py
+++ b/spacy/cli/ud_train.py
@@ -247,12 +247,18 @@ Token.set_extension('inside_fused', default=False)
 ##################
 
 
-def load_nlp(corpus, config):
+def load_nlp(corpus, config, vectors=None):
     lang = corpus.split('_')[0]
     nlp = spacy.blank(lang)
     if config.vectors:
-        nlp.vocab.from_disk(Path(config.vectors) / 'vocab')
+        if not vectors:     
+            raise ValueError("config asks for vectors, but no vectors "
+                             "directory set on command line (use -v)")
+        if (Path(vectors) / corpus).exists():
+            nlp.vocab.from_disk(Path(vectors) / corpus / 'vocab')
+    nlp.meta['treebank'] = corpus
     return nlp
+                                                                            
 
 def initialize_pipeline(nlp, docs, golds, config, device):
     nlp.add_pipe(nlp.create_pipe('parser'))
@@ -274,10 +280,12 @@ def initialize_pipeline(nlp, docs, golds, config, device):
 
 class Config(object):
     def __init__(self, vectors=None, max_doc_length=10, multitask_tag=True,
-            multitask_sent=True, nr_epoch=30, batch_size=1000, dropout=0.2):
+                multitask_sent=True, multitask_dep=True, multitask_vectors=False,
+                nr_epoch=30, batch_size=1000, dropout=0.2):
         for key, value in locals().items():
             setattr(self, key, value)
-
+                                                                 
+    
     @classmethod
     def load(cls, loc):
         with Path(loc).open('r', encoding='utf8') as file_:
@@ -319,9 +327,11 @@ class TreebankPaths(object):
     parses_dir=("Directory to write the development parses", "positional", None, Path),
     config=("Path to json formatted config file", "positional"),
     limit=("Size limit", "option", "n", int),
-    use_gpu=("Use GPU", "option", "g", int)
+    use_gpu=("Use GPU", "option", "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):
+def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1, vectors_dir=None):
     spacy.util.fix_random_seed()
     lang.zh.Chinese.Defaults.use_jieba = False
     lang.ja.Japanese.Defaults.use_janome = False
@@ -331,7 +341,7 @@ def main(ud_dir, parses_dir, config, corpus, limit=0, use_gpu=-1):
     if not (parses_dir / corpus).exists():
         (parses_dir / corpus).mkdir()
     print("Train and evaluate", corpus, "using lang", paths.lang)
-    nlp = load_nlp(paths.lang, config)
+    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)