Recalculate alignment if tokenization differs (#5868)

* Recalculate alignment if tokenization differs

* Refactor cached alignment data

spacy/gold/example.pxd

@@ -4,4 +4,6 @@ from ..tokens.doc cimport Doc
 cdef class Example:
     cdef readonly Doc x
     cdef readonly Doc y
-    cdef readonly object _alignment
+    cdef readonly object _cached_alignment
+    cdef readonly object _cached_words_x
+    cdef readonly object _cached_words_y

spacy/gold/example.pyx

@@ -32,9 +32,9 @@ cdef class Example:
             raise TypeError(Errors.E972.format(arg="predicted"))
         if reference is None:
             raise TypeError(Errors.E972.format(arg="reference"))
-        self.x = predicted
-        self.y = reference
-        self._alignment = alignment
+        self.predicted = predicted
+        self.reference = reference
+        self._cached_alignment = alignment
 
     def __len__(self):
         return len(self.predicted)
@@ -45,7 +45,8 @@ cdef class Example:
 
         def __set__(self, doc):
             self.x = doc
-            self._alignment = None
+            self._cached_alignment = None
+            self._cached_words_x = [t.text for t in doc]
 
     property reference:
         def __get__(self):
@@ -53,7 +54,8 @@ cdef class Example:
 
         def __set__(self, doc):
             self.y = doc
-            self._alignment = None
+            self._cached_alignment = None
+            self._cached_words_y = [t.text for t in doc]
 
     def copy(self):
         return Example(
@@ -79,13 +81,15 @@ cdef class Example:
 
     @property
     def alignment(self):
-        if self._alignment is None:
-            spacy_words = [token.orth_ for token in self.predicted]
-            gold_words = [token.orth_ for token in self.reference]
-            if gold_words == []:
-                gold_words = spacy_words
-            self._alignment = Alignment.from_strings(spacy_words, gold_words)
-        return self._alignment
+        words_x = [token.text for token in self.x]
+        words_y = [token.text for token in self.y]
+        if self._cached_alignment is None or \
+                words_x != self._cached_words_x or \
+                words_y != self._cached_words_y:
+            self._cached_alignment = Alignment.from_strings(words_x, words_y)
+            self._cached_words_x = words_x
+            self._cached_words_y = words_y
+        return self._cached_alignment
 
     def get_aligned(self, field, as_string=False):
         """Return an aligned array for a token attribute."""

spacy/tests/test_gold.py

@@ -655,3 +655,18 @@ def test_split_sents(merged_dict):
     assert token_annotation_2["words"] == ["It", "is", "just", "me"]
     assert token_annotation_2["tags"] == ["PRON", "AUX", "ADV", "PRON"]
     assert token_annotation_2["sent_starts"] == [1, 0, 0, 0]
+
+
+def test_retokenized_docs(doc):
+    a = doc.to_array(["TAG"])
+    doc1 = Doc(doc.vocab, words=[t.text for t in doc]).from_array(["TAG"], a)
+    doc2 = Doc(doc.vocab, words=[t.text for t in doc]).from_array(["TAG"], a)
+    example = Example(doc1, doc2)
+
+    assert example.get_aligned("ORTH", as_string=True) == ['Sarah', "'s", 'sister', 'flew', 'to', 'Silicon', 'Valley', 'via', 'London', '.']
+
+    with doc1.retokenize() as retokenizer:
+        retokenizer.merge(doc1[0:2])
+        retokenizer.merge(doc1[5:7])
+
+    assert example.get_aligned("ORTH", as_string=True) == [None, 'sister', 'flew', 'to', None, 'via', 'London', '.']