Add multi-task objective for sentence segmentation

spacy/pipeline.pyx

@@ -624,11 +624,13 @@ class MultitaskObjective(Tagger):
             self.make_label = self.make_dep_tag_offset
         elif target == 'ent_tag':
             self.make_label = self.make_ent_tag
+        elif target == 'sent_start':
+            self.make_label = self.make_sent_start
         elif hasattr(target, '__call__'):
             self.make_label = target
         else:
             raise ValueError("MultitaskObjective target should be function or "
-                             "one of: dep, tag, ent, dep_tag_offset, ent_tag.")
+                             "one of: dep, tag, ent, sent_start, dep_tag_offset, ent_tag.")
         self.cfg = dict(cfg)
         self.cfg.setdefault('cnn_maxout_pieces', 2)
         self.cfg.setdefault('pretrained_dims',
@@ -737,6 +739,52 @@ class MultitaskObjective(Tagger):
         else:
             return '%s-%s' % (tags[i], ents[i])
 
+    @staticmethod
+    def make_sent_start(target, words, tags, heads, deps, ents, cache=True, _cache={}):
+        '''A multi-task objective for representing sentence boundaries,
+        using BILU scheme. (O is impossible)
+
+        The implementation of this method uses an internal cache that relies
+        on the identity of the heads array, to avoid requiring a new piece
+        of gold data. You can pass cache=False if you know the cache will
+        do the wrong thing.
+        '''
+        if cache:
+            if id(heads) in _cache:
+                return _cache[id(heads)][target]
+            else:
+                for key in list(_cache.keys()):
+                    _cache.pop(key)
+            sent_tags = ['I-SENT'] * len(words)
+            _cache[id(heads)] = sent_tags
+        else:
+            sent_tags = ['I-SENT'] * len(words)
+
+        def _find_root(child):
+            while heads[child] != child:
+                if heads[child] is None:
+                    if child == 0:
+                        return child
+                    else:
+                        child -= 1
+                else:
+                    child = heads[child]
+            return child
+
+        sentences = {}
+        for i in range(len(words)):
+            root = _find_root(i)
+            sentences.setdefault(root, []).append(i)
+        for root, span in sorted(sentences.items()):
+            if len(span) == 1:
+                sent_tags[span[0]] = 'U-SENT'
+            else:
+                sent_tags[span[0]] = 'B-SENT'
+                sent_tags[span[-1]] = 'L-SENT'
+        return sent_tags[target]
+
+
+
 
 class SimilarityHook(Pipe):
     """