diff --git a/spacy/coref_scorer.py b/spacy/coref_scorer.py
index b266ec3b3..981b1cf03 100644
--- a/spacy/coref_scorer.py
+++ b/spacy/coref_scorer.py
@@ -9,14 +9,14 @@ def get_cluster_info(predicted_clusters, gold_clusters):
     return (gold_clusters, predicted_clusters, g2p, p2g)
 
 
-def get_markable_assignments(inp_clusters, out_clusters):
+def get_markable_assignments(in_clusters, out_clusters):
     markable_cluster_ids = {}
     out_dic = {}
     for cluster_id, cluster in enumerate(out_clusters):
         for m in cluster:
             out_dic[m] = cluster_id
 
-    for cluster in inp_clusters:
+    for cluster in in_clusters:
         for im in cluster:
             for om in out_dic:
                 if im == om:
diff --git a/spacy/ml/models/coref.py b/spacy/ml/models/coref.py
index ca9011577..4be22dd96 100644
--- a/spacy/ml/models/coref.py
+++ b/spacy/ml/models/coref.py
@@ -29,8 +29,8 @@ def build_wl_coref_model(
         dim = 768
 
     with Model.define_operators({">>": chain}):
-        coref_scorer = PyTorchWrapper(
-            CorefScorer(
+        coref_clusterer = PyTorchWrapper(
+            CorefClusterer(
                 dim,
                 distance_embedding_size,
                 hidden_size,
@@ -39,14 +39,14 @@ def build_wl_coref_model(
                 antecedent_limit,
                 antecedent_batch_size,
             ),
-            convert_inputs=convert_coref_scorer_inputs,
-            convert_outputs=convert_coref_scorer_outputs,
+            convert_inputs=convert_coref_clusterer_inputs,
+            convert_outputs=convert_coref_clusterer_outputs,
         )
-        coref_model = tok2vec >> coref_scorer
+        coref_model = tok2vec >> coref_clusterer
     return coref_model
 
 
-def convert_coref_scorer_inputs(
+def convert_coref_clusterer_inputs(
     model: Model,
     X: List[Floats2d],
     is_train: bool
@@ -65,7 +65,7 @@ def convert_coref_scorer_inputs(
     return ArgsKwargs(args=(word_features, ), kwargs={}), backprop
 
 
-def convert_coref_scorer_outputs(model: Model, inputs_outputs, is_train: bool):
+def convert_coref_clusterer_outputs(model: Model, inputs_outputs, is_train: bool):
     _, outputs = inputs_outputs
     scores, indices = outputs
 
@@ -81,7 +81,7 @@ def convert_coref_scorer_outputs(model: Model, inputs_outputs, is_train: bool):
     return (scores_xp, indices_xp), convert_for_torch_backward
 
 
-class CorefScorer(torch.nn.Module):
+class CorefClusterer(torch.nn.Module):
     """
     Combines all coref modules together to find coreferent token pairs.
     Submodules (in the order of their usage in the pipeline):
diff --git a/spacy/ml/models/span_predictor.py b/spacy/ml/models/span_predictor.py
index 1ded9c3c7..03101edf9 100644
--- a/spacy/ml/models/span_predictor.py
+++ b/spacy/ml/models/span_predictor.py
@@ -48,7 +48,7 @@ def build_span_predictor(
 
 
 def convert_span_predictor_inputs(
-    model: Model, X: Tuple[Ints1d, Floats2d, Ints1d], is_train: bool
+    model: Model, X: Tuple[Ints1d, Tuple[Floats2d, Ints1d]], is_train: bool
 ):
     tok2vec, (sent_ids, head_ids) = X
     # Normally we should use the input is_train, but for these two it's not relevant