Added majority picking for generated slots with prompt ensemble

prompt-learning/prompt_decode.py

@@ -1,4 +1,6 @@
 import argparse
+import collections
+
 import numpy as np
 import os
 import json
@@ -11,6 +13,9 @@ from prompt_utils import get_ensemble_prompts
 from metrics import PromptDSTEvaluator
 from datetime import datetime
 
+SLOT_PICK_HIGHEST_PROB = "highest"
+SLOT_PICK_SIMPLE_MAJORITY = "majority"
+
 
 def set_seed(args):
     np.random.seed(args.seed)
@@ -19,11 +24,11 @@ def set_seed(args):
         torch.cuda.manual_seed_all(args.seed)
 
 
-# Use this for generating next word (Testing)
+# Use this for generating slot <- Inference (Testing)
 def generate_slot_from_prompt(args, history, value, tokenizer, model, device):
     # check if prompt ensemble is enabled in arguments
     if args.with_prompt_ensemble:
-        return generate_slot_with_prompt_ensemble(history, value, tokenizer, model, device)
+        return generate_slot_with_prompt_ensemble(args, history, value, tokenizer, model, device)
 
     # get value-based prompt for generating slots
     prompt = get_value_based_prompt(value)
@@ -43,7 +48,7 @@ def generate_slot_from_prompt(args, history, value, tokenizer, model, device):
     return generated_word.strip().lower()
 
 
-def generate_slot_with_prompt_ensemble(history, value, tokenizer, model, device):
+def generate_slot_with_prompt_ensemble(args, history, value, tokenizer, model, device):
     # get prompts for ensemble generation
     prompts = get_ensemble_prompts(value)
 
@@ -73,9 +78,27 @@ def generate_slot_with_prompt_ensemble(history, value, tokenizer, model, device)
         gen_probs.append(gen_prob.item())
         gen_words.append(gen_word)
 
-    # return word with the highest probability
+    if args.ensemble_pick_slot == SLOT_PICK_HIGHEST_PROB:
-    generated_word = gen_words[gen_probs.index(max(gen_probs))]
+        # return the slot with the highest probability
-    return generated_word.strip().lower()
+        generated_word = gen_words[gen_probs.index(max(gen_probs))]
+        return generated_word.strip().lower()
+    elif args.ensemble_pick_slot == SLOT_PICK_SIMPLE_MAJORITY:
+
+        # do a simple majority voting for generated slots
+        # if there's no simple majority, pick the slot with the highest probability
+        word_counter = collections.Counter(gen_words)
+        max_slot = max(word_counter, key=word_counter.get)
+        max_slot_count = word_counter[max_slot]
+
+        if max_slot_count >= 3 or (max_slot_count == 2 and len(word_counter) > 2):
+            # winner slot (simple majority)
+            generated_word = max_slot
+        else:
+            # generated slot with the highest probability
+            generated_word = gen_words[gen_probs.index(max(gen_probs))]
+
+        # return the generated slot
+        return generated_word.strip().lower()
 
 
 def main():
@@ -93,6 +116,8 @@ def main():
     parser.add_argument('--seed', type=int, default=42, help="random seed for initialization")
     parser.add_argument("--with_prompt_ensemble", action="store_true",
                         help="Flag for enabling/disabling prompt ensembling while generating")
+    parser.add_argument("--ensemble_pick_slot", type=str, default=SLOT_PICK_SIMPLE_MAJORITY,
+                        help="Flag for setting the algorithm to pick the generated slot from ensemble outputs")
     parser.add_argument("--with_answered_prompts", action="store_true",
                         help="Flag to enable/disable the use of answered prompts while generating")
 

prompt-learning/prompt_utils.py

@@ -6,13 +6,18 @@ TYPE_PROMPT_ENSEMBLE = "prompt-ensemble"
 
 PROMPT_WEIGHT = 0.25
 
+INVERSE_PROMPTS = {
+    "i1": "belief states: $slot = $value",
+    "i2": "belief states: slot = $slot, value = $value",
+}
+
 PROMPT_TEMPLATES = {
     "value-based": {
         "training": "belief states: value = $value, slot = $slot",
         "generate": "belief states: value = $value, slot ="
     },
     "inverse-prompt": {
-        "training": "belief states: $slot = $value",
+        "training": INVERSE_PROMPTS["i2"],
     },
     "prompt-ensemble": {
         "training": {

prompt-learning/train_prompting.sh

@@ -49,10 +49,11 @@ SAVE_DIR="${SAVED_MODELS_PROMPT}"/"${experiment_folder}"
 echo "Trained Models (epochs) will be saved in ${SAVE_DIR}"
 
 # different number of epoch for different training sets
+# when using prompt ensemble for training, preferably use more number of epochs.
 if [ "$data_split" = "5-dpd" ] || [ "$data_split" = "10-dpd" ]; then
-  epochs=7
+  epochs=5
 else
-  epochs=10
+  epochs=8
 fi
 
 python prompt_train.py \