Allow multitask objectives to be added to the parser and NER more easily

spacy/pipeline.pyx

@@ -881,15 +881,17 @@ cdef class DependencyParser(Parser):
     @property
     def postprocesses(self):
         return [nonproj.deprojectivize]
+    
+    def add_multitask_objective(self, target):
+        labeller = MultitaskObjective(self.vocab, target=target)
+        self._multitasks.append(labeller)
 
     def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
-        for target in []:
-            labeller = MultitaskObjective(self.vocab, target=target)
+        for labeller in self._multitasks:
             tok2vec = self.model[0]
             labeller.begin_training(gold_tuples, pipeline=pipeline,
                                     tok2vec=tok2vec, sgd=sgd)
-            pipeline.append(labeller)
-            self._multitasks.append(labeller)
+            pipeline.append((labeller.name, labeller))
 
     def __reduce__(self):
         return (DependencyParser, (self.vocab, self.moves, self.model),
@@ -901,15 +903,17 @@ cdef class EntityRecognizer(Parser):
     TransitionSystem = BiluoPushDown
 
     nr_feature = 6
+    
+    def add_multitask_objective(self, target):
+        labeller = MultitaskObjective(self.vocab, target=target)
+        self._multitasks.append(labeller)
 
     def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
-        for target in []:
-            labeller = MultitaskObjective(self.vocab, target=target)
+        for labeller in self._multitasks:
             tok2vec = self.model[0]
             labeller.begin_training(gold_tuples, pipeline=pipeline,
                                     tok2vec=tok2vec)
-            pipeline.append(labeller)
-            self._multitasks.append(labeller)
+            pipeline.append((labeller.name, labeller))
 
     def __reduce__(self):
         return (EntityRecognizer, (self.vocab, self.moves, self.model),

spacy/syntax/nn_parser.pyx

@@ -269,9 +269,6 @@ cdef class Parser:
                 zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
             )
 
-        # TODO: This is an unfortunate hack atm!
-        # Used to set input dimensions in network.
-        lower.begin_training(lower.ops.allocate((500, token_vector_width)))
         cfg = {
             'nr_class': nr_class,
             'hidden_depth': depth,
@@ -840,8 +837,14 @@ cdef class Parser:
             self.cfg.update(cfg)
         elif sgd is None:
             sgd = self.create_optimizer()
+        self.model[1].begin_training(
+            self.model[1].ops.allocate((5, cfg['token_vector_width'])))
         return sgd
 
+    def add_multitask_objective(self, target):
+        # Defined in subclasses, to avoid circular import
+        raise NotImplementedError
+    
     def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
         '''Setup models for secondary objectives, to benefit from multi-task
         learning. This method is intended to be overridden by subclasses.