Fix Entity Linker with tokenization mismatches (fix #9575) (#10457)

* Add failing test

* Partial fix for issue

This kind of works. The issue with token length mismatches is gone. The
problem is that when you get empty lists of encodings to compare, it
fails because the sizes are not the same, even though they're both zero:
(0, 3) vs (0,). Not sure why that happens...

* Short circuit on empties

* Remove spurious check

The check here isn't needed now the the short circuit is fixed.

* Update spacy/tests/pipeline/test_entity_linker.py

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Use "eg", not "example"

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

spacy/pipeline/entity_linker.py

@@ -234,10 +234,11 @@ class EntityLinker(TrainablePipe):
         nO = self.kb.entity_vector_length
         doc_sample = []
         vector_sample = []
-        for example in islice(get_examples(), 10):
-            doc = example.x
+        for eg in islice(get_examples(), 10):
+            doc = eg.x
             if self.use_gold_ents:
-                doc.ents = example.y.ents
+                ents, _ = eg.get_aligned_ents_and_ner()
+                doc.ents = ents
             doc_sample.append(doc)
             vector_sample.append(self.model.ops.alloc1f(nO))
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
@@ -312,7 +313,8 @@ class EntityLinker(TrainablePipe):
 
         for doc, ex in zip(docs, examples):
             if self.use_gold_ents:
-                doc.ents = ex.reference.ents
+                ents, _ = ex.get_aligned_ents_and_ner()
+                doc.ents = ents
             else:
                 # only keep matching ents
                 doc.ents = ex.get_matching_ents()
@@ -345,7 +347,7 @@ class EntityLinker(TrainablePipe):
         for eg in examples:
             kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
 
-            for ent in eg.reference.ents:
+            for ent in eg.get_matching_ents():
                 kb_id = kb_ids[ent.start]
                 if kb_id:
                     entity_encoding = self.kb.get_vector(kb_id)
@@ -356,7 +358,11 @@ class EntityLinker(TrainablePipe):
         entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
         selected_encodings = sentence_encodings[keep_ents]
 
-        # If the entity encodings list is empty, then
+        # if there are no matches, short circuit
+        if not keep_ents:
+            out = self.model.ops.alloc2f(*sentence_encodings.shape)
+            return 0, out
+
         if selected_encodings.shape != entity_encodings.shape:
             err = Errors.E147.format(
                 method="get_loss", msg="gold entities do not match up"

spacy/tests/pipeline/test_entity_linker.py

@@ -14,7 +14,7 @@ from spacy.pipeline.legacy import EntityLinker_v1
 from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
 from spacy.scorer import Scorer
 from spacy.tests.util import make_tempdir
-from spacy.tokens import Span
+from spacy.tokens import Span, Doc
 from spacy.training import Example
 from spacy.util import ensure_path
 from spacy.vocab import Vocab
@@ -1075,3 +1075,32 @@ def test_no_gold_ents(patterns):
 
     # this will run the pipeline on the examples and shouldn't crash
     results = nlp.evaluate(train_examples)
+
+@pytest.mark.issue(9575)
+def test_tokenization_mismatch():
+    nlp = English()
+    # include a matching entity so that update isn't skipped
+    doc1 = Doc(nlp.vocab, words=["Kirby", "123456"], spaces=[True, False], ents=["B-CHARACTER", "B-CARDINAL"])
+    doc2 = Doc(nlp.vocab, words=["Kirby", "123", "456"], spaces=[True, False, False], ents=["B-CHARACTER", "B-CARDINAL", "B-CARDINAL"])
+
+    eg = Example(doc1, doc2)
+    train_examples = [eg]
+    vector_length = 3
+
+    def create_kb(vocab):
+        # create placeholder KB
+        mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
+        mykb.add_entity(entity="Q613241", freq=12, entity_vector=[6, -4, 3])
+        mykb.add_alias("Kirby", ["Q613241"], [0.9])
+        return mykb
+
+    entity_linker = nlp.add_pipe("entity_linker", last=True)
+    entity_linker.set_kb(create_kb)
+
+    optimizer = nlp.initialize(get_examples=lambda: train_examples)
+    for i in range(2):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+
+    nlp.add_pipe("sentencizer", first=True)
+    results = nlp.evaluate(train_examples)