Contextual Information with Neural NLU

I made the necessary changes for catching the contextual information from the given text. Also after running the moviebot to load the necessary slot values I am also adding extra slot values for contextual information.

data/movies_domain.yaml

@@ -13,3 +13,7 @@ slot_names:
     cover: ["slots_not_required_NLU"]
     imdb_link: ["slots_not_required_NLU"]
     title: ["user_requestable", "slots_annotation"]
+    time: ["slots_annotation"]
+    companion: ["slots_annotation"]
+    location: ["slots_annotation"]
+

data/movies_ontology.json

@@ -26,7 +26,10 @@
         "actors",
         "directors",
         "year",
-        "title"
+        "title",
+        "time",
+        "companion",
+        "location"
     ],
     "multiple_values": [
         "genres"

data/training/utterances.yaml

@@ -499,7 +499,19 @@ REVEAL:
   - "[I almost never watch](modifier) films directed by [Claude Chabrol](directors)."
   - "[I often find solace with](modifier) films from the [late 2050s](year)."
   - "[Adventure drama](genres) films [always take me on an emotional journey](modifier)."
-
+  - "[I love](modifier) watching movies on a [rainy night](time)."
+  - "[I dislike with a passion](modifier) to watch a movie on [mondays](time)."
+  - "[I do enjoy](modifier) a nice movie with [my wife](companion)."
+  - "[My granddaughters](companion) and [I always watch](modifier) [Micheal Douglas](actors) movies together."
+  - "[I absolutely adore](modifier) watching [Tom Hanks](actors) movies on [late evenings](time) with my [best friend](companion)."
+  - "[I can't resist](modifier) watching [Meryl Streep](actors) movies on [weekend afternoons](time) with my [boyfriend](companion)."
+  - "[I find it relaxing](modifier) to watch [Leonardo DiCaprio](actors) movies on [Sunday mornings](time) with my [significant other](companion)."
+  - "[I'm not too keen](modifier) on watching [Emma Stone](actors) movies on [early mornings](time) with my [siblings](companion)."
+  - "[I always look forward](modifier) to watching [Denzel Washington](actors) movies on [Friday nights](time) with my [parents](companion)."
+  - "[I'm hooked on](modifier) watching movies with my [roommate](companion) on [weekend nights](time) on [HBO](location)."
+  - "[I enjoy](modifier) watching [Tom Cruise](actors) movies on [Sunday afternoons](time) on [television](location)."
+  - "[I savor](modifier) watching [Sacha Baron Cohen](actors) movies on [Thursday evenings](time) with my [friends](companion) on my [laptop](location)."
+
 INQUIRE:
   - "What [genre](genres) does this movie fall under?"
   - "Is this a [comedy](genres) or [drama](genres)?"

moviebot/nlu/annotation/joint_bert/slot_mapping.py

@@ -45,6 +45,12 @@ class JointBERTSlot(EnumWithMapping):
     I_PREFERENCE_KEYWORDS = auto()
     B_PREFERENCE_YEAR = auto()
     I_PREFERENCE_YEAR = auto()
+    B_PREFERENCE_TIME = auto()
+    I_PREFERENCE_TIME = auto()
+    B_PREFERENCE_COMPANION = auto()
+    I_PREFERENCE_COMPANION = auto()
+    B_PREFERENCE_LOCATION = auto()
+    I_PREFERENCE_LOCATION = auto()
     B_INQUIRE_GENRES = auto()
     I_INQUIRE_GENRES = auto()
     B_INQUIRE_RATING = auto()

moviebot/nlu/neural_nlu.py

@@ -60,7 +60,7 @@ def generate_dacts(
         if selected_option:
             return selected_option
 
-        intent, slots = self.annotate_utterance(user_utterance)
+        intent, slots, contents = self.annotate_utterance(user_utterance)
         intent = UserIntents[intent]
 
         constraints = []
@@ -90,7 +90,7 @@ def generate_dacts(
 
     def annotate_utterance(
         self, user_utterance: UserUtterance
-    ) -> Tuple[str, list]:
+    ) -> Tuple[str, list, list]:
         """Annotates the utterance with intent and slot information.
 
         Args:
@@ -99,6 +99,7 @@ def annotate_utterance(
         Returns:
             A tuple of the intent and slot information.
         """
+        available_contexts = ["PREFERENCE_COMPANION","PREFERENCE_TIME","PREFERENECE_LOCATION"]
         mask = [
             not token.startswith("##")
             for token in self._tokenizer.tokenize(user_utterance.text)
@@ -125,6 +126,7 @@ def annotate_utterance(
 
         # For each starting point, find the end point (i.e., all 'I_' labels)
         slots_info = []
+        context_info = []
         for start in start_indices:
             end = start
             while (
@@ -136,16 +138,27 @@ def annotate_utterance(
             char_start = offset_mapping[start][0]
             char_end = offset_mapping[end][1]
             slot_value = user_utterance.text[char_start:char_end]
-            slots_info.append(
-                {
-                    "slot": JointBERTSlot.from_index(slot_idxs[start]).name[2:],
-                    "value": slot_value,
-                    "start": char_start,
-                    "end": char_end,
-                }
-            )
+            if JointBERTSlot.from_index(slot_idxs[start]).name[2:] in available_contexts:
+                context_info.append(
+                    {
+                        "context": JointBERTSlot.from_index(slot_idxs[start]).name[2:],
+                        "value": slot_value,
+                        "start": char_start,
+                        "end": char_end,   
+                    }
+
+                )            
+            else:    
+                slots_info.append(
+                    {
+                        "slot": JointBERTSlot.from_index(slot_idxs[start]).name[2:],
+                        "value": slot_value,
+                        "start": char_start,
+                        "end": char_end,
+                    }
+                )
 
-        return intent, slots_info
+        return intent, slots_info, context_info
 
     def get_constraint_operator(self, text: str) -> Operator:
         """Gets the operator based on the text. Only supports negation for now.
@@ -163,6 +176,9 @@ def get_constraint_operator(self, text: str) -> Operator:
 if __name__ == "__main__":
     nlu = NeuralNLU(None)
 
+    user_utterance = UserUtterance("I like space movies") 
+    intent, slots_info, context_info = nlu.annotate_utterance(user_utterance)
+    """
     class DS:
         item_in_focus = None
 
@@ -173,3 +189,4 @@ class DS:
         )
 
         print([str(da) for da in da])
+    """

tests/nlu/test_neural_nlu.py

@@ -0,0 +1,197 @@
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+
+from moviebot.nlu.neural_nlu import NeuralNLU
+from moviebot.core.utterance.utterance import UserUtterance
+from moviebot.core.core_types import DialogueOptions
+from moviebot.dialogue_manager.dialogue_state import DialogueState
+from tests.mocks.mock_data_loader import MockDataLoader
+
+from moviebot.dialogue_manager.dialogue_act import DialogueAct
+from moviebot.core.intents.user_intents import UserIntents
+from moviebot.nlu.annotation.item_constraint import ItemConstraint
+from moviebot.core.intents.agent_intents import AgentIntents
+from moviebot.nlu.annotation.operator import Operator
+from moviebot.nlu.annotation.values import Values
+
+
+@pytest.fixture
+def dialogue_state():
+    dialogue_state = Mock()
+    dialogue_state.item_in_focus = None
+    dialogue_state.last_agent_dacts = []
+    return dialogue_state
+
+@pytest.fixture
+@patch("moviebot.nlu.user_intents_checker.DataLoader", new=MockDataLoader)
+def nlu():
+    config = {
+        "domain": "",
+        "database": "",
+        "slot_values_path": "",
+        "tag_words_slots_path": "",
+    }
+    nlu = NeuralNLU(config)
+    return nlu 
+
+
+@pytest.mark.parametrize(
+    "last_dacts", [[], [DialogueAct(AgentIntents.ACKNOWLEDGE, [])]]
+)
+def test_generate_dacts(nlu,dialogue_state,last_dacts): 
+    user_utterance = UserUtterance("I want to watch an action movie")
+    dialogue_state.last_agent_dacts = last_dacts
+    options = {}
+
+    dacts = nlu.generate_dacts(user_utterance, options, dialogue_state)
+
+    assert len(dacts) == 1
+    assert dacts[0].intent == UserIntents.REVEAL
+
+def test_annotate_utterance(nlu):
+    user_utterance = UserUtterance("I want to watch an action movie") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 2
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I want"
+    assert slots_info[1]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[1]["value"] == "action"
+
+    user_utterance = UserUtterance("Thank you for the recommendations,goodbye")
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "BYE"
+    assert len(slots_info) == 0
+
+    user_utterance = UserUtterance("Is this a comedy movie ?") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "INQUIRE"
+    assert len(slots_info) == 1
+    assert slots_info[0]["slot"] == "INQUIRE_GENRES"
+    assert slots_info[0]["value"] == "comedy"
+
+
+    user_utterance = UserUtterance("Recommend me a movie with Brad Pitt") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REMOVE_PREFERENCE"
+    assert len(slots_info) == 1
+    assert slots_info[0]["slot"] == "PREFERENCE_ACTORS"
+    assert slots_info[0]["value"] == "Brad Pitt"
+
+
+    user_utterance = UserUtterance("I like space movies") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 2
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I like"
+    assert slots_info[1]["slot"] == "PREFERENCE_KEYWORDS"
+    assert slots_info[1]["value"] == "space"
+
+    user_utterance = UserUtterance("I hate horror movies") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 2
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I hate"
+    assert slots_info[1]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[1]["value"] == "horror"
+    assert len(context_info) == 0
+
+
+    user_utterance = UserUtterance("I love watching movies on a rainy night") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 1
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I love"
+    assert len(context_info) == 1
+    assert context_info[0]["context"] == "PREFERENCE_TIME"
+    assert context_info[0]["value"] == "rainy night"
+
+    user_utterance = UserUtterance("I love watching drama movies on a rainy night") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 2
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I love"
+    assert slots_info[1]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[1]["value"] == "drama"
+    assert len(context_info) == 1
+    assert context_info[0]["context"] == "PREFERENCE_TIME"
+    assert context_info[0]["value"] == "rainy night"
+
+    user_utterance = UserUtterance("I really enjoy watching drama movies on a rainy night") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 2
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I really enjoy"
+    assert slots_info[1]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[1]["value"] == "drama"
+    assert len(context_info) == 1
+    assert context_info[0]["context"] == "PREFERENCE_TIME"
+    assert context_info[0]["value"] == "rainy night"
+
+    user_utterance = UserUtterance("I'll be going on a date with my girlfriend this thursday evening and I need to find a good horror movie.") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "UNK"
+    assert len(slots_info) == 1
+    assert slots_info[0]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[0]["value"] == "horror"
+    assert len(context_info) == 2
+    assert context_info[0]["context"] == "PREFERENCE_COMPANION"
+    assert context_info[0]["value"] == "girlfriend"
+    assert context_info[1]["context"] == "PREFERENCE_TIME"
+    assert context_info[1]["value"] == "thursday evening"
+
+    user_utterance = UserUtterance("I hate christmas movies") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 2
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I hate"
+    assert slots_info[1]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[1]["value"] == "christmas"
+    assert len(context_info) == 0
+
+    user_utterance = UserUtterance("I hate watching movies at christmas") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "REVEAL"
+    assert len(slots_info) == 1
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I hate"    
+    assert len(context_info) == 1
+    assert context_info[0]["context"] == "PREFERENCE_TIME"
+    assert context_info[0]["value"] == "christmas"
+
+    user_utterance = UserUtterance("I am looking for an action movie to watch with my siblings for the sunday afternoon.We love Brad Pitt. Can you recommend me something ?") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "UNK"
+    assert len(slots_info) == 3
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I am"
+    assert slots_info[1]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[1]["value"] == "action"     
+    assert slots_info[2]["slot"] == "PREFERENCE_ACTORS"
+    assert slots_info[2]["value"] == "Brad Pitt"        
+    assert len(context_info) == 2
+    assert context_info[0]["context"] == "PREFERENCE_COMPANION"
+    assert context_info[0]["value"] == "siblings"
+    assert context_info[1]["context"] == "PREFERENCE_TIME"
+    assert context_info[1]["value"] == "sunday afternoon"
+
+    user_utterance = UserUtterance("I am looking for an action movie to watch with my siblings.We love Brad Pitt. Can you recommend me something ?") 
+    intent, slots_info,context_info = nlu.annotate_utterance(user_utterance)
+    assert intent == "UNK"
+    assert len(slots_info) == 3
+    assert slots_info[0]["slot"] == "PREFERENCE_MODIFIER"
+    assert slots_info[0]["value"] == "I am looking"
+    assert slots_info[1]["slot"] == "PREFERENCE_GENRES"
+    assert slots_info[1]["value"] == "action"     
+    assert slots_info[2]["slot"] == "PREFERENCE_ACTORS"
+    assert slots_info[2]["value"] == "Brad Pitt"        
+    assert len(context_info) == 1
+    assert context_info[0]["context"] == "PREFERENCE_COMPANION"
+    assert context_info[0]["value"] == "siblings"