Added output files and results in the README

README.md

@@ -231,24 +231,25 @@ Example: `sh test_prompting.sh -m 50-dpd/experiment-20221003T172424/epoch-09`
 The generated belief states (outputs) are saved under `OUTPUTS_DIR_PROMPT` folder. Some of the best outputs are uploaded to this repository and can be found under [outputs](outputs) folder. 
 
 ### Prompting Evaluation  
-The standard Joint Goal Accuracy (JGA) is used to evaluate the belief predictions. 
+The standard Joint Goal Accuracy (**JGA**) is used to evaluate the belief state predictions. In order to exclude the influence of wrongly extracted values, **JGA*** is computed only for values that are extracted correctly at each turn.
   
-Edit the [evaluate.py](prompt-learning/evaluate.py) to set the predictions output file before running the evaluation 
+The [evaluate.py](prompt-learning/evaluate.py) file can be used to verify the below JGA scores.
 ```shell  
-python evaluate.py
+cd prompt-learning
+python evaluate.py -o path/to/outputs/file
 ```  
 
 ### Results from prompt-based belief state generations
-|data-split| JGA* |  
-|--|:--:|  
-| 5-dpd | //TODO |  
-| 10-dpd | //TODO |  
-| 50-dpd | //TODO |  
-| 100-dpd | //TODO |  
-| 125-dpd | //TODO |  
-| 250-dpd | //TODO |
-
-// TODO :: Add prompt-based outputs and results in the above table
+|data-split| JGA | JGA* |  
+|--|:--:|:--:|  
+| 5-dpd | 30.66 | 71.04 |  
+| 10-dpd | 42.65 | 86.43 |  
+| 50-dpd | 47.06 | 91.63 |  
+| 100-dpd | 47.74 | 92.31 |  
+| 125-dpd | 46.49 | 91.86 |  
+| 250-dpd | 47.06 | 92.08 |  
+
+> **Note:** All the generated output files for the above reported results are available in the repository. Check [outputs/prompt-learning](outputs/prompt-learning) directory to see the output JSON files for each data-split.
 
 ## Multi-prompt Learning Experiments
 

baseline/evaluate.py

@@ -119,7 +119,7 @@ class BaselineDSTEvaluator:
         print('Evaluation :: Joint Goal Accuracy = ', (correctly_predicted / total_turns) * 100)
 
 
-evaluator = BaselineDSTEvaluator('../outputs/baseline/50-dpd/checkpoint-55000/output_test.json',
+evaluator = BaselineDSTEvaluator('../outputs/baseline/50-dpd/output_test.json',
                                  '../data/baseline/test/test.soloist.json')
 predicted_belief_states = evaluator.parse_prediction_belief_states()
 true_belief_states = evaluator.parse_true_belief_states()

prompt-learning/evaluate.py

@@ -0,0 +1,34 @@
+import argparse
+import os
+from metrics import PromptDSTEvaluator
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    # Required parameters
+    parser.add_argument('-o', '--output_file', default=None, type=str, required=True,
+                        help="The path of the outputs JSON file <JSON Path>.")
+
+    # parse the arguments
+    args = parser.parse_args()
+
+    if args.output_file is None:
+        print('No output file provided for evaluation!')
+        return
+
+    # Assertion check for file availability
+    assert os.path.isfile(args.output_file)
+
+    # create an evaluator instance for Prompt-based DST
+    evaluator = PromptDSTEvaluator(args.output_file)
+
+    # compute Joint Goal Accuracy
+    evaluator.compute_joint_goal_accuracy()
+
+    # compute JGA for the values that are correctly extracted in a turn
+    evaluator.compute_jga_for_correct_values()
+
+
+if __name__ == "__main__":
+    main()

prompt-learning/metrics.py

@@ -50,10 +50,10 @@ class PromptDSTEvaluator:
 
     def compute_jga_for_correct_values(self, no_print=False):
         if not no_print:
-            print('Computing Joint Goal Accuracy metric only where values are extracted correctly!')
+            print('Computing Joint Goal Accuracy metric for the values that are extracted correctly! (turn-level)')
 
         if len(self.true_states_list) != len(self.gen_states_list):
-            raise ValueError('Length mismatch!')
+            raise ValueError('Unable to compute the metric. Length mismatch in the outputs!')
 
         # keep a count for computing JGA
         correctly_generated, total_turns = 0, 0