diff --git a/spacy/tagger.pxd b/spacy/tagger.pxd
index e8f6357b0..da0e2493e 100644
--- a/spacy/tagger.pxd
+++ b/spacy/tagger.pxd
@@ -18,7 +18,7 @@ cpdef enum TagType:
 cdef class Tagger:
     cpdef int set_tags(self, Tokens tokens) except -1
     cpdef class_t predict(self, int i, Tokens tokens) except 0
-    cpdef int tell_answer(self, class_t gold) except -1
+    cpdef int tell_answer(self, list gold) except -1
  
     cpdef readonly Pool mem
     cpdef readonly Extractor extractor
diff --git a/spacy/tagger.pyx b/spacy/tagger.pyx
index f2263c88d..6962e42cf 100644
--- a/spacy/tagger.pyx
+++ b/spacy/tagger.pyx
@@ -45,7 +45,7 @@ def train(train_sents, model_dir, nr_iter=10):
                 guess = tagger.predict(i, tokens)
                 tokens.set_tag(i, tagger.tag_type, guess)
                 if gold != NULL_TAG:
-                    tagger.tell_answer(gold)
+                    tagger.tell_answer([gold])
                     total += 1
                     n_corr += guess == gold
                 #print('%s\t%d\t%d' % (tokens[i].string, guess, gold))
@@ -116,7 +116,7 @@ cdef class Tagger:
         self._guess = self.model.score(self._scores, self._feats, self._values)
         return self._guess
 
-    cpdef int tell_answer(self, class_t gold) except -1:
+    cpdef int tell_answer(self, list golds) except -1:
         """Provide the correct tag for the word the tagger was last asked to predict.
         During Tagger.predict, the tagger remembers the features and prediction
         for the example. These are used to calculate a weight update given the
@@ -130,11 +130,17 @@ cdef class Tagger:
         >>> EN.pos_tagger.tell_answer(JJ)
         """
         cdef class_t guess = self._guess
-        if gold == guess or gold == NULL_TAG:
+        if guess in golds:
             self.model.update({})
             return 0
-        counts = {guess: {}, gold: {}}
-        self.extractor.count(counts[gold], self._feats, 1)
+        best_gold = golds[0]
+        best_score = self._scores[best_gold-1]
+        for gold in golds[1:]:
+            if self._scores[gold-1] > best_gold:
+                best_score = self._scores[best_gold-1]
+                best_gold = gold
+        counts = {guess: {}, best_gold: {}}
+        self.extractor.count(counts[best_gold], self._feats, 1)
         self.extractor.count(counts[guess], self._feats, -1)
         self.model.update(counts)