diff --git a/scispacy/abbreviation.py b/scispacy/abbreviation.py
index 732b068..cf5e4aa 100644
--- a/scispacy/abbreviation.py
+++ b/scispacy/abbreviation.py
@@ -151,14 +151,22 @@ class AbbreviationDetector:
 
     nlp: `Language`, a required argument for spacy to use this as a factory
     name: `str`, a required argument for spacy to use this as a factory
+    make_serializable: `bool`, a required argument for whether we want to use the serializable
+    or non serializable version.
     """
 
-    def __init__(self, nlp: Language, name: str = "abbreviation_detector") -> None:
+    def __init__(
+        self,
+        nlp: Language,
+        name: str = "abbreviation_detector",
+        make_serializable: bool = False,
+    ) -> None:
         Doc.set_extension("abbreviations", default=[], force=True)
         Span.set_extension("long_form", default=None, force=True)
 
         self.matcher = Matcher(nlp.vocab)
         self.matcher.add("parenthesis", [[{"ORTH": "("}, {"OP": "+"}, {"ORTH": ")"}]])
+        self.make_serializable = make_serializable
         self.global_matcher = Matcher(nlp.vocab)
 
     def find(self, span: Span, doc: Doc) -> Tuple[Span, Set[Span]]:
@@ -186,6 +194,12 @@ def __call__(self, doc: Doc) -> Doc:
             for short in short_forms:
                 short._.long_form = long_form
                 doc._.abbreviations.append(short)
+        if self.make_serializable:
+            abbreviations = doc._.abbreviations
+            doc._.abbreviations = [
+                self.make_short_form_serializable(abbreviation)
+                for abbreviation in abbreviations
+            ]
         return doc
 
     def find_matches_for(
@@ -223,3 +237,24 @@ def find_matches_for(
             self.global_matcher.remove(key)
 
         return list((k, v) for k, v in all_occurences.items())
+
+    def make_short_form_serializable(self, abbreviation: Span):
+        """
+        Converts the abbreviations into a short form that is serializable to enable multiprocessing
+
+        Parameters
+        ----------
+        abbreviation: Span
+            The abbreviation span identified by the detector
+        """
+        long_form = abbreviation._.long_form
+        abbreviation._.long_form = long_form.text
+        serializable_abbr = {
+            "short_text": abbreviation.text,
+            "short_start": abbreviation.start,
+            "short_end": abbreviation.end,
+            "long_text": long_form.text,
+            "long_start": long_form.start,
+            "long_end": long_form.end,
+        }
+        return serializable_abbr
diff --git a/scispacy/linking.py b/scispacy/linking.py
index 974bbea..1e247bd 100644
--- a/scispacy/linking.py
+++ b/scispacy/linking.py
@@ -96,20 +96,23 @@ def __init__(
         self.umls = self.kb
 
     def __call__(self, doc: Doc) -> Doc:
-        mentions = []
+        mention_strings = []
         if self.resolve_abbreviations and Doc.has_extension("abbreviations"):
-
+            # TODO: This is possibly sub-optimal - we might
+            # prefer to look up both the long and short forms.
             for ent in doc.ents:
-                # TODO: This is possibly sub-optimal - we might
-                # prefer to look up both the long and short forms.
-                if ent._.long_form is not None:
-                    mentions.append(ent._.long_form)
+                if isinstance(ent._.long_form, Span):
+                    # Long form
+                    mention_strings.append(ent._.long_form.text)
+                elif isinstance(ent._.long_form, str):
+                    # Long form
+                    mention_strings.append(ent._.long_form)
                 else:
-                    mentions.append(ent)
+                    # no abbreviations case
+                    mention_strings.append(ent.text)
         else:
-            mentions = doc.ents
+            mention_strings = [x.text for x in doc.ents]
 
-        mention_strings = [x.text for x in mentions]
         batch_candidates = self.candidate_generator(mention_strings, self.k)
 
         for mention, candidates in zip(doc.ents, batch_candidates):
diff --git a/tests/conftest.py b/tests/conftest.py
index 68622fb..705dfe1 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,4 +1,4 @@
-from typing import Dict, Tuple
+from typing import Dict, Tuple, Optional
 import os
 
 import pytest
@@ -8,6 +8,7 @@
 
 from scispacy.custom_sentence_segmenter import pysbd_sentencizer
 from scispacy.custom_tokenizer import combined_rule_tokenizer, combined_rule_prefixes, remove_new_lines
+from scispacy.abbreviation import AbbreviationDetector
 
 LOADED_SPACY_MODELS: Dict[Tuple[str, bool, bool, bool], SpacyModelType] = {}
 
@@ -19,6 +20,7 @@ def get_spacy_model(
     ner: bool,
     with_custom_tokenizer: bool = False,
     with_sentence_segmenter: bool = False,
+    with_serializable_abbreviation_detector: Optional[bool] = None,
 ) -> SpacyModelType:
     """
     In order to avoid loading spacy models repeatedly,
@@ -26,7 +28,7 @@ def get_spacy_model(
     we used to create the spacy model, so any particular
     configuration only gets loaded once.
     """
-    options = (spacy_model_name, pos_tags, parse, ner, with_custom_tokenizer, with_sentence_segmenter)
+    options = (spacy_model_name, pos_tags, parse, ner, with_custom_tokenizer, with_sentence_segmenter, with_serializable_abbreviation_detector)
     if options not in LOADED_SPACY_MODELS:
         disable = ["vectors", "textcat"]
         if not pos_tags:
@@ -46,6 +48,8 @@ def get_spacy_model(
             spacy_model.tokenizer = combined_rule_tokenizer(spacy_model)
         if with_sentence_segmenter:
             spacy_model.add_pipe("pysbd_sentencizer", first=True)
+        if with_serializable_abbreviation_detector is not None:
+            spacy_model.add_pipe("abbreviation_detector", config={"make_serializable": with_serializable_abbreviation_detector})
 
         LOADED_SPACY_MODELS[options] = spacy_model
     return LOADED_SPACY_MODELS[options]
@@ -97,9 +101,13 @@ def test_model_dir():
 
 @pytest.fixture()
 def combined_all_model_fixture():
-    nlp = get_spacy_model("en_core_sci_sm", True, True, True, with_custom_tokenizer=True, with_sentence_segmenter=False)
+    nlp = get_spacy_model("en_core_sci_sm", True, True, True, with_custom_tokenizer=True, with_sentence_segmenter=False, with_serializable_abbreviation_detector=True)
     return nlp
 
+@pytest.fixture()
+def combined_all_model_fixture_non_serializable_abbrev():
+    nlp = get_spacy_model("en_core_sci_sm", True, True, True, with_custom_tokenizer=True, with_sentence_segmenter=False, with_serializable_abbreviation_detector=False)
+    return nlp
 
 @pytest.fixture()
 def combined_rule_prefixes_fixture():
diff --git a/tests/custom_tests/test_all_model.py b/tests/custom_tests/test_all_model.py
index 56991a3..1da5d0e 100644
--- a/tests/custom_tests/test_all_model.py
+++ b/tests/custom_tests/test_all_model.py
@@ -3,6 +3,7 @@
 import spacy
 from spacy.vocab import Vocab
 import shutil
+import pytest
 
 
 def test_custom_segmentation(combined_all_model_fixture):
@@ -36,6 +37,31 @@ def test_custom_segmentation(combined_all_model_fixture):
     ]
     actual_tokens = [t.text for t in doc]
     assert expected_tokens == actual_tokens
-    assert doc.is_parsed
+    assert doc.has_annotation("DEP")
     assert doc[0].dep_ == "ROOT"
     assert doc[0].tag_ == "NN"
+
+def test_full_pipe_serializable(combined_all_model_fixture):
+    text = "Induction of cytokine expression in leukocytes (CEIL) by binding of thrombin-stimulated platelets. BACKGROUND: Activated platelets tether and activate myeloid leukocytes."
+    doc = [doc for doc in combined_all_model_fixture.pipe([text, text], n_process = 2)][0]
+    # If we got here this means that both model is serializable and there is an abbreviation that would break if it wasn't
+    assert len(doc._.abbreviations) > 0
+    abbrev = doc._.abbreviations[0]
+    assert abbrev["short_text"] == "CEIL"
+    assert abbrev["long_text"] == "cytokine expression in leukocytes"
+    assert doc[abbrev["short_start"] : abbrev["short_end"]].text == abbrev["short_text"]
+    assert doc[abbrev["long_start"] : abbrev["long_end"]].text == abbrev["long_text"]
+
+def test_full_pipe_not_serializable(combined_all_model_fixture_non_serializable_abbrev):
+    text = "Induction of cytokine expression in leukocytes (CEIL) by binding of thrombin-stimulated platelets. BACKGROUND: Activated platelets tether and activate myeloid leukocytes."
+    # This line requires the pipeline to be serializable, so the test should fail here
+    doc = combined_all_model_fixture_non_serializable_abbrev(text)
+    with pytest.raises(TypeError):
+        doc.to_bytes()
+
+# Below is the test version to be used once we move to spacy v3.1.0 or higher
+# def test_full_pipe_not_serializable(combined_all_model_fixture_non_serializable_abbrev):
+#     text = "Induction of cytokine expression in leukocytes (CEIL) by binding of thrombin-stimulated platelets. BACKGROUND: Activated platelets tether and activate myeloid leukocytes."
+#     # This line requires the pipeline to be serializable (because it uses 2 processes), so the test should fail here
+#     with pytest.raises(TypeError):
+#         list(combined_all_model_fixture_non_serializable_abbrev.pipe([text, text], n_process = 2))
\ No newline at end of file