diff --git a/spacy/language.py b/spacy/language.py
index 7adae0ed5..9dde3c1a9 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -192,7 +192,7 @@ class Language(object):
             name = getattr(proc, 'name', None)
             if name in disable:
                 continue
-            proc(doc)
+            doc = proc(doc)
         return doc
 
     def update(self, docs, golds, drop=0., sgd=None, losses=None):
diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index 724891c9b..95d4a144d 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -73,17 +73,16 @@ class TokenVectorEncoder(object):
         self.doc2feats = doc2feats()
         self.model = model
 
-    def __call__(self, docs):
+    def __call__(self, doc):
         """Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM
         model. Vectors are set to the `Doc.tensor` attribute.
 
         docs (Doc or iterable): One or more documents to add vectors to.
         RETURNS (dict or None): Intermediate computations.
         """
-        if isinstance(docs, Doc):
-            docs = [docs]
-        tokvecses = self.predict(docs)
-        self.set_annotations(docs, tokvecses)
+        tokvecses = self.predict([doc])
+        self.set_annotations([doc], tokvecses)
+        return doc
 
     def pipe(self, stream, batch_size=128, n_threads=-1):
         """Process `Doc` objects as a stream.
@@ -169,6 +168,7 @@ class NeuralTagger(object):
     def __call__(self, doc):
         tags = self.predict([doc.tensor])
         self.set_annotations([doc], tags)
+        return doc
 
     def pipe(self, stream, batch_size=128, n_threads=-1):
         for docs in cytoolz.partition_all(batch_size, stream):
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 095dcc5e7..6723821d7 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -305,8 +305,9 @@ cdef class Parser:
         Returns:
             None
         """
-        states = self.parse_batch([doc], doc.tensor)
-        self.set_annotations(doc, states[0])
+        states = self.parse_batch([doc], [doc.tensor])
+        self.set_annotations([doc], states)
+        return doc
 
     def pipe(self, docs, int batch_size=1000, int n_threads=2):
         """