diff --git a/Evaluation/evaluator.py b/Evaluation/evaluator.py
index 61c9b0a58..754e5d13d 100644
--- a/Evaluation/evaluator.py
+++ b/Evaluation/evaluator.py
@@ -107,9 +107,25 @@
         gold = gold[gold["Article_From"] == args.score]
 
         logger.info(f"Evaluation limited to {sys.shape} events from source {args.score}")
-    assert len(sys.sort_values("Event_ID")["Event_ID"].to_list()) == len(
-        gold.sort_values("Event_ID")["Event_ID"].to_list()
-    ), f"Missing events! {set(sys.sort_values('Event_ID')['Event_ID'].to_list()) ^ set(gold.sort_values('Event_ID')['Event_ID'].to_list())}"
+
+    # Add dummy rows for missing events
+    missing_ids = set(sys['Event_ID'].to_list()) ^ set(gold['Event_ID'].to_list())
+    if missing_ids:
+        logger.info(f"Missing events! {missing_ids}. The columns in these events will be constructed with `NoneType` objects. The system output will be penalized for missing events with the selected null penalty ({args.null_penalty})")
+        gold_cols = list(gold.columns)
+        rows_to_add = []
+        for event_id in missing_ids:
+            # Create a dictionary for the new row with all columns set to "" except Country_Norm which excepts a list
+            new_row = {col: None for col in gold_cols}
+            for col in ["Country_Norm", "Location_Norm"]:
+                if col in gold_cols:
+                    new_row[col] = "[]"
+            new_row['Event_ID'] = event_id  # Set the 'Event_ID'
+            rows_to_add.append(new_row)
+
+        missing_rows = pd.DataFrame(rows_to_add)
+        sys = pd.concat([sys, missing_rows], ignore_index=True).sort_values('Event_ID')
+        sys.replace({np.nan: None}, inplace=True)
 
     # Specify null penalty
     null_penalty = args.null_penalty
@@ -132,9 +148,11 @@
     sys = sys.sort_values("Event_ID")
     gold = gold.sort_values("Event_ID")
 
-    for col in ["Country_Norm"]:
-        sys[col] = sys[col].apply(ast.literal_eval)
-        gold[col] = gold[col].apply(ast.literal_eval)
+    for col in ["Country_Norm", "Location_Norm"]:
+        if col in sys.columns:
+            sys[col] = sys[col].apply(ast.literal_eval)
+        if col in gold.columns:
+            gold[col] = gold[col].apply(ast.literal_eval)
 
     logger.info("Parsed strings to lists or dicts")