Update jmultiwoz.py

jmultiwoz.py

@@ -38,7 +38,8 @@ _CITATION = """\
 _DESCRIPTION = """\
 JMultiWOZ is a large-scale Japanese multi-domain task-oriented dialogue dataset. The dataset is collected using 
 the Wizard-of-Oz (WoZ) methodology, where two human annotators simulate the user and the system. The dataset contains 
-4,264 dialogues across 6 domains, including restaurant, hotel, attraction, shopping, taxi, and weather. 
+4,246 dialogues across 6 domains, including restaurant, hotel, attraction, shopping, taxi, and weather. Available 
+annotations include user goal, dialogue state, and utterances.
 """
 
 # TODO: Add a link to an official homepage for the dataset here
@@ -51,11 +52,12 @@ _LICENSE = "CC BY-ND 4.0"
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
-    "original_zip": "https://github.com/ohashi56225/jmultiwoz-evaluation/raw/master/dataset/JMultiWOZ_1.0.zip",
+    # "original_zip": "https://github.com/ohashi56225/jmultiwoz-evaluation/raw/master/dataset/JMultiWOZ_1.0.zip",
+    "original_zip": "JMultiWOZ_1.0.zip"
 }
 
 
-def _flatten_value(values):
+def _flatten_value(values) -> str:
     if not isinstance(values, list):
         return values
     flat_values = [
@@ -99,13 +101,13 @@ class JMultiWOZDataset(datasets.GeneratorBasedBuilder):
                         "slot": datasets.Value("string"),
                         "value": datasets.Value("string"),
                     }),
-                    "db_result": datasets.Sequence({
+                    "db_result": {
                         "candidate_entities": datasets.Sequence(datasets.Value("string")),
                         "active_entity": datasets.Sequence({
                             "slot": datasets.Value("string"),
                             "value": datasets.Value("string"),
                         })
-                    }),
+                    },
                     "book_result": datasets.Sequence({
                         "domain": datasets.Value("string"),
                         "success": datasets.Value("string"),
@@ -152,7 +154,6 @@ class JMultiWOZDataset(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["train"]],
-                    "split": "train",
                 },
             ),
             datasets.SplitGenerator(
@@ -160,7 +161,6 @@ class JMultiWOZDataset(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["dev"]],
-                    "split": "dev",
                 },
             ),
             datasets.SplitGenerator(
@@ -168,13 +168,12 @@ class JMultiWOZDataset(datasets.GeneratorBasedBuilder):
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "dialogues": [dialogues[dialogue_name] for dialogue_name in split_list["test"]],
-                    "split": "test"
                 },
             ),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, dialogues, split):
+    def _generate_examples(self, dialogues):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         
@@ -216,48 +215,46 @@ class JMultiWOZDataset(datasets.GeneratorBasedBuilder):
                     "dialogue_state": {
                         "belief_state": [],
                         "book_state": [],
-                        "db_result": [],
+                        "db_result": {},
                         "book_result": [],
                     },
                 }
-                if turn["speaker"] == "USER":
-                    continue
+                if turn["speaker"] == "SYSTEM":
+                    for domain, slots in turn["dialogue_state"]["belief_state"].items():
+                        for slot, value in slots.items():
+                            example_turn["dialogue_state"]["belief_state"].append({
+                                "domain": domain,
+                                "slot": slot,
+                                "value": value,
+                            })
 
-                for domain, slots in turn["dialogue_state"]["belief_state"].items():
-                    for slot, value in slots.items():
-                        example_turn["dialogue_state"]["belief_state"].append({
-                            "domain": domain,
+                    for domain, slots in turn["dialogue_state"]["book_state"].items():
+                        for slot, value in slots.items():
+                            example_turn["dialogue_state"]["book_state"].append({
+                                "domain": domain,
+                                "slot": slot,
+                                "value": value,
+                            })
+                    
+                    candidate_entities = turn["dialogue_state"]["db_result"]["candidate_entities"]
+                    active_entity = turn["dialogue_state"]["db_result"]["active_entity"]
+                    if not active_entity:
+                        active_entity = {}
+                    example_turn["dialogue_state"]["db_result"] = {
+                        "candidate_entities":candidate_entities,
+                        "active_entity": [{
                             "slot": slot,
-                            "value": value,
-                        })
-
-                for domain, slots in turn["dialogue_state"]["book_state"].items():
-                    for slot, value in slots.items():
-                        example_turn["dialogue_state"]["book_state"].append({
+                            "value": _flatten_value(value),
+                        } for slot, value in active_entity.items()]
+                    }
+                    
+                    for domain, result in turn["dialogue_state"]["book_result"].items():
+                        example_turn["dialogue_state"]["book_result"].append({
                             "domain": domain,
-                            "slot": slot,
-                            "value": value,
+                            "success": result["success"],
+                            "ref": result["ref"],
                         })
                 
-                candidate_entities = turn["dialogue_state"]["db_result"]["candidate_entities"]
-                active_entity = turn["dialogue_state"]["db_result"]["active_entity"]
-                if not active_entity:
-                    active_entity = {}
-                example_turn["dialogue_state"]["db_result"].append({
-                    "candidate_entities":candidate_entities,
-                    "active_entity": [{
-                        "slot": slot,
-                        "value": _flatten_value(value),
-                    } for slot, value in active_entity.items()]
-                })
-                
-                for domain, result in turn["dialogue_state"]["book_result"].items():
-                    example_turn["dialogue_state"]["book_result"].append({
-                        "domain": domain,
-                        "success": result["success"],
-                        "ref": result["ref"],
-                    })
-                
                 example["turns"].append(example_turn)
 
             yield id_, example