diff --git a/spacy/tests/parser/test_ner.py b/spacy/tests/parser/test_ner.py
index 587d1fff1..efc7ebc1b 100644
--- a/spacy/tests/parser/test_ner.py
+++ b/spacy/tests/parser/test_ner.py
@@ -131,6 +131,41 @@ def test_negative_sample_key_is_in_config(vocab, entity_types):
     assert tsys.cfg["neg_key"] == "non_entities"
 
 
+@pytest.mark.filterwarnings("ignore::UserWarning")
+def test_get_oracle_moves_negative_entities(tsys, doc, entity_annots):
+    entity_annots = [(s, e, "!" + label) for s, e, label in entity_annots]
+    example = Example.from_dict(doc, {"entities": entity_annots})
+    ex_dict = example.to_dict()
+
+    for i, tag in enumerate(ex_dict["doc_annotation"]["entities"]):
+        if tag == "L-!GPE":
+            ex_dict["doc_annotation"]["entities"][i] = "-"
+    example = Example.from_dict(doc, ex_dict)
+
+    act_classes = tsys.get_oracle_sequence(example)
+    names = [tsys.get_class_name(act) for act in act_classes]
+    assert names
+
+
+def test_get_oracle_moves_negative_entities2(tsys, vocab):
+    doc = Doc(vocab, words=["A", "B", "C", "D"])
+    entity_annots = ["B-!PERSON", "L-!PERSON", "B-!PERSON", "L-!PERSON"]
+    example = Example.from_dict(doc, {"entities": entity_annots})
+    act_classes = tsys.get_oracle_sequence(example)
+    names = [tsys.get_class_name(act) for act in act_classes]
+    assert names
+
+
+@pytest.mark.skip(reason="Maybe outdated? Unsure")
+def test_get_oracle_moves_negative_O(tsys, vocab):
+    doc = Doc(vocab, words=["A", "B", "C", "D"])
+    entity_annots = ["O", "!O", "O", "!O"]
+    example = Example.from_dict(doc, {"entities": entity_annots})
+    act_classes = tsys.get_oracle_sequence(example)
+    names = [tsys.get_class_name(act) for act in act_classes]
+    assert names
+
+
 # We can't easily represent this on a Doc object. Not sure what the best solution
 # would be, but I don't think it's an important use case?
 @pytest.mark.skip(reason="No longer supported")
@@ -242,7 +277,7 @@ def test_train_empty():
         train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
     ner = nlp.add_pipe("ner", last=True)
     ner.add_label("PERSON")
-    nlp.initialize()
+    nlp.initialize(get_examples=lambda: train_examples)
     for itn in range(2):
         losses = {}
         batches = util.minibatch(train_examples, size=8)
@@ -372,7 +407,7 @@ def test_block_ner():
 def test_overfitting_IO():
     # Simple test to try and quickly overfit the NER component
     nlp = English()
-    ner = nlp.add_pipe("ner")
+    ner = nlp.add_pipe("ner", config={"model": {}})
     train_examples = []
     for text, annotations in TRAIN_DATA:
         train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))