diff --git a/spacy/training/example.pyx b/spacy/training/example.pyx
index 008be1b99..ee613f5ac 100644
--- a/spacy/training/example.pyx
+++ b/spacy/training/example.pyx
@@ -176,24 +176,47 @@ cdef class Example:
     def get_aligned_parse(self, projectivize=True):
         cand_to_gold = self.alignment.x2y
         gold_to_cand = self.alignment.y2x
-        aligned_heads = [None] * self.x.length
-        aligned_deps = [None] * self.x.length
-        has_deps = [token.has_dep() for token in self.y]
-        has_heads = [token.has_head() for token in self.y]
         heads = [token.head.i for token in self.y]
         deps = [token.dep_ for token in self.y]
+
         if projectivize:
             proj_heads, proj_deps = nonproj.projectivize(heads, deps)
+            has_deps = [token.has_dep() for token in self.y]
+            has_heads = [token.has_head() for token in self.y]
+
             # ensure that missing data remains missing
             heads = [h if has_heads[i] else heads[i] for i, h in enumerate(proj_heads)]
             deps = [d if has_deps[i] else deps[i] for i, d in enumerate(proj_deps)]
-        for cand_i in range(self.x.length):
-            if cand_to_gold.lengths[cand_i] == 1:
-                gold_i = cand_to_gold[cand_i][0]
-                if gold_to_cand.lengths[heads[gold_i]] == 1:
-                    aligned_heads[cand_i] = int(gold_to_cand[heads[gold_i]][0])
-                    aligned_deps[cand_i] = deps[gold_i]
-        return aligned_heads, aligned_deps
+
+        # Select all candidate tokens that are aligned to a single gold token.
+        c2g_single_toks = numpy.where(cand_to_gold.lengths == 1)[0]
+
+        # Fetch all aligned gold token incides.
+        if c2g_single_toks.shape == cand_to_gold.lengths.shape:
+            # This the most likely case.
+            gold_i = cand_to_gold[:].squeeze()
+        else:
+            gold_i = numpy.vectorize(lambda x: cand_to_gold[int(x)][0])(c2g_single_toks).squeeze()
+
+        # Fetch indices of all gold heads for the aligned gold tokens.
+        heads = numpy.asarray(heads, dtype='i')
+        gold_head_i = heads[gold_i]
+
+        # Select all gold tokens that are heads of the previously selected 
+        # gold tokens (and are aligned to a single candidate token).
+        g2c_len_heads = gold_to_cand.lengths[gold_head_i]
+        g2c_len_heads = numpy.where(g2c_len_heads == 1)[0]
+        g2c_i = numpy.vectorize(lambda x: gold_to_cand[int(x)][0])(gold_head_i[g2c_len_heads]).squeeze()
+
+        # Update head/dep alignments with the above.
+        aligned_heads = numpy.full((self.x.length), None)
+        aligned_heads[c2g_single_toks[g2c_len_heads]] = g2c_i
+
+        deps = numpy.asarray(deps)
+        aligned_deps = numpy.full((self.x.length), None)
+        aligned_deps[c2g_single_toks] = deps[gold_i]
+
+        return aligned_heads.tolist(), aligned_deps.tolist()
 
     def get_aligned_sent_starts(self):
         """Get list of SENT_START attributes aligned to the predicted tokenization.