unify NLU and NLG model names

Author: Guangsen Wang

botsim/models/nlg/nlg_model.py

@@ -8,7 +8,7 @@ class ModelNLG(NLG):
     def __init__(self, model, *args):
         self.model = model
 
-    def generate(self, *args):
-        """ generate a natural language message
+    def generate(self, dialog_state, *args):
+        """ generate a natural language message given a semantic-level dialog state
         """
-        pass
+        raise NotImplementedError

botsim/models/nlu/nlu_api.py

@@ -7,9 +7,9 @@
 
 
 class APIIntentPredictor(IntentDetector):
-    def __init__(self, api_end_point, *args):
+    def __init__(self, dialog_act_map, api_end_point, *args):
+        super().__init__(dialog_act_map)
         self.api_end_point = api_end_point
-        self.intents = None
 
-    def predict(self, user_message, *args):
-        pass
+    def predict(self, bot_message, dialog_name, *args):
+        raise NotImplementedError

botsim/models/nlu/nlu_base.py

@@ -3,13 +3,18 @@
 #   SPDX-License-Identifier: BSD-3-Clause
 #   For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
 
+from botsim.botsim_utils.utils import load_intent_examples
 class IntentDetector:
-    def __init__(self, *args):
-        self.intents = None
-
-    def predict(self, bot_message, *args):
+    def __init__(self, dialog_act_map_path):
+        self.intent_templates = None
+        if dialog_act_map_path:
+            dialog_act_maps = load_intent_examples(dialog_act_map_path)
+            self.intent_templates = []
+            for intent in dialog_act_maps:
+                self.intent_templates.append({"intent": intent, "dialog_act_and_slot": dialog_act_maps[intent]})
+    def predict(self, bot_message, dialog_name, *args):
         """
-        Predict the intent labels given the user message
+        Predict the intent labels given the bot message via template-matching
         :param bot_message: bot message/prompt for the intent query
         """
         raise NotImplementedError

botsim/models/nlu/nlu_fuzzy_match.py

@@ -6,21 +6,15 @@
 import re
 from rapidfuzz import process, fuzz
 
-from botsim.botsim_utils.utils import load_intent_examples
 from botsim.models.nlu.nlu_base import IntentDetector
 
 
 class FuzzyMatchIntentPredictor(IntentDetector):
-    def __init__(self, dialog_act_map_path=None, *args):
-        self.intents = None
-        if dialog_act_map_path:
-            dialog_act_maps = load_intent_examples(dialog_act_map_path)
-            self.intents = []
-            for intent in dialog_act_maps:
-                self.intents.append({"intent": intent, "dialog_act_and_slot": dialog_act_maps[intent]})
+    def __init__(self, dialog_act_map_path=None):
+        super().__init__(dialog_act_map_path)
 
-    def predict(self, bot_message, intent_name):
-        intents = self.intents
+    def predict(self, bot_message, dialog_name):
+        intents = self.intent_templates
         # remove variable names between $$
         regex = r"\$.*? "
         bot_message = re.sub(regex, "$", bot_message)
@@ -33,7 +27,7 @@ def predict(self, bot_message, intent_name):
         score_to_candidates = {}
 
         for intent_index, intent_info in enumerate(intents):
-            if intent_name.find(intent_info["intent"]) == -1:
+            if dialog_name.find(intent_info["intent"]) == -1:
                 continue
             dialog_act_and_slots = intent_info["dialog_act_and_slot"]
             res = []

botsim/models/nlu/nlu_model.py

@@ -7,11 +7,8 @@
 
 
 class ModelIntentPredictor(IntentDetector):
-    def __init__(self, model_path, *args):
-        self.model_path = model_path
-        self.intents = None
-        self.model = None
+    def __init__(self, dialog_act_map_path, model_path, *args):
+        super().__init__(dialog_act_map_path)
 
-    def predict(self, user_message, *args):
-        if self.model:
-            return self.model(user_message)
+    def predict(self, bot_message, dialog_name):
+        raise NotImplementedError

botsim/modules/simulator/abus.py

@@ -31,12 +31,10 @@ def __init__(self, simulation_goals, simulation_configs):
         self.max_round = simulation_configs["simulator"]["run_time"]["max_round_num"]
         self.default_key = botsim_default_key
 
-        # self.intent = \
-        self.intent_model = \
+        self.nlu_model = \
             FuzzyMatchIntentPredictor( simulation_configs["generator"]["file_paths"]["revised_dialog_act_map"])
 
-        #FuzzyMatchIntentPredictor(simulation_configs["generator"]["file_paths"]["revised_dialog_act_map"])
-        self.template_nlg = TemplateNLG(simulation_configs["generator"]["file_paths"]["response_template"])
+        self.nlg_model = TemplateNLG(simulation_configs["generator"]["file_paths"]["response_template"])
 
         # a stack for keeping track of BotSIM dialog turns. Each element includes
         # 1. user dialog acts

botsim/modules/simulator/user_simulator.py

@@ -47,7 +47,7 @@ def generate_user_response(self, botsim_action):
         """
         nl_message, semantic_message = "", ""
         if botsim_action["action"] != "fail":
-            template_responses, slots_responses = self.template_nlg.generate(botsim_action, "user")
+            template_responses, slots_responses = self.nlg_model.generate(botsim_action, "user")
             if isinstance(template_responses[0], list):
                 for i, responses in enumerate(template_responses):
                     j = random.choice(range(0, len(responses)))
@@ -341,7 +341,7 @@ def terminate_simulation_session_from_message(self,
                 and not self.state["intent_succeed"]:
             self.state["request_slots"]["fall_back"] = "UNK"
             bot_message = " ".join(bot_response)
-            best_matching_dialog_act, _, _, _ = self.intent_model.predict(bot_message, self.goal["name"])
+            best_matching_dialog_act, _, _, _ = self.nlu_model.predict(bot_message, self.goal["name"])
             if best_matching_dialog_act == "":
                 return {"to_discard": True}
             self.state["intent_error"] = (best_matching_dialog_act, self.state["user_response"], bot_message)
@@ -504,12 +504,12 @@ def enqueue_bot_actions_from_bot_messages(self,
         for bot_message in bot_api_response:
             # process one message in the bot api response list
             best_matching_dialog_act, best_matching_message, best_matching_score, matched_dialog_acts = \
-                self.intent_model.predict(bot_message, self.goal["name"])
+                self.nlu_model.predict(bot_message, self.goal["name"])
             if bot_action["round"] == self.intent_check_turn_index:
                 # check for intent errors on the intent_check_turn
-                for intent_index, task in enumerate(self.intent_model.intents):
+                for intent_index, task in enumerate(self.nlu_model.intent_templates):
                     if task["intent"] == self.goal["name"]: continue
-                    _, _, match_score, _ = self.intent_model.predict(bot_message, task["intent"])
+                    _, _, match_score, _ = self.nlu_model.predict(bot_message, task["intent"])
                     if match_score > best_matching_score:
                         self.state["request_slots"]["fall_back"] = "UNK"
                         self.state["intent_error"] = \