diff --git a/stanza/models/constituency/parser_training.py b/stanza/models/constituency/parser_training.py
index 59bb6d090..86f904a36 100644
--- a/stanza/models/constituency/parser_training.py
+++ b/stanza/models/constituency/parser_training.py
@@ -447,6 +447,8 @@ def iterate_training(args, trainer, train_trees, train_sequences, transitions, d
             "Epoch %d finished" % trainer.epochs_trained,
             "Transitions correct:   %d" % total_correct,
             "Transitions incorrect: %d" % total_incorrect,
+            "Transition loss for epoch: %.5f" % epoch_stats.transition_loss,
+            "Similarity loss for epoch: %.5f" % epoch_stats.similarity_loss,
             "Total loss for epoch: %.5f" % epoch_stats.total_loss,
             "Dev score      (%5d): %8f" % (trainer.epochs_trained, f1),
             "Best dev score (%5d): %8f" % (trainer.best_epoch, trainer.best_f1)
@@ -597,18 +599,20 @@ def train_model_one_batch(epoch, batch_idx, model, training_batch, transition_te
         gold_results = model.analyze_trees([x.tree for x in training_batch], keep_output_layers=True)
         errors = [error_analysis_in_order.analyze_tree(result.gold, result.predictions[0].tree) for result in reparsed_results]
 
-        similarities_inputs = []
-        similarities_targets = []
+        similarity_inputs = []
+        similarity_targets = []
         for reparsed, gold, first_error in zip(reparsed_results, gold_results, errors):
             if reparsed.predictions[0].tree == reparsed.gold:
                 continue
             error_type, gold_index, pred_index = error_analysis_in_order.analyze_tree(reparsed.gold, reparsed.predictions[0].tree)
             if gold_index is None or pred_index is None:
                 continue
-            similarities_inputs.append(reparsed.output_layers[pred_index])
-            similarities_targets.append(gold.output_layers[gold_index])
-        if len(similarities_inputs) > 0:
-            similarity_loss = similarity_loss_function(torch.stack(similarities_inputs), torch.stack(similarities_targets))
+            similarity_inputs.append(reparsed.output_layers[pred_index])
+            similarity_targets.append(gold.output_layers[gold_index])
+        if len(similarity_inputs) > 0:
+            similarity_inputs = torch.stack(similarity_inputs)
+            similarity_targets = torch.stack(similarity_targets)
+            similarity_loss = similarity_loss_function(similarity_inputs, similarity_targets) * args['similarity_learning_rate']
 
     all_errors = []
     all_answers = []