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indosmd.py

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+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+_CITATION = """\
+@article{kautsar2023indotod,
+    author={Kautsar, Muhammad Dehan Al and Nurdini, Rahmah Khoirussyifa' and Cahyawijaya, Samuel and Winata, Genta Indra and Purwarianti, Ayu},
+    title={IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems},
+    journal={arXiv preprint arXiv:2311.00958},
+    year={2023},
+}
+"""
+
+_LANGUAGES = ["ind"]
+_LOCAL = False
+
+_DATASETNAME = "indosmd"
+
+_DESCRIPTION = """\
+IndoSMD is a synthetic task-oriented dialogue system dataset that was translated from the In-Car Assistant (SMD) dataset (Eric et al., 2017) into the new Indonesian dataset using the translation pipeline method
+including delexicalization, translation, and delexicalization. The dataset consists of 323 dialogues in the POI Navigation, Calendar Scheduling, and Weather Information Retrieval domain, with a user and an agent talking to each other.
+It also consists of slots and dialogue acts from the user and the agent.
+"""
+
+_HOMEPAGE = "https://github.com/dehanalkautsar/IndoToD/tree/main/IndoSMD"
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://raw.githubusercontent.com/dehanalkautsar/IndoToD/main/IndoSMD/IndoSMD_split/IndoSMD_train.json",
+        "validation": "https://raw.githubusercontent.com/dehanalkautsar/IndoToD/main/IndoSMD/IndoSMD_split/IndoSMD_dev.json",
+        "test": "https://raw.githubusercontent.com/dehanalkautsar/IndoToD/main/IndoSMD/IndoSMD_split/IndoSMD_test.json",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.E2E_TASK_ORIENTED_DIALOGUE]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IndoSMDDataset(datasets.GeneratorBasedBuilder):
+    """IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description="IndoToD: IndoSMD source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_tod",
+            version=SEACROWD_VERSION,
+            description="IndoToD: IndoSMD SEACrowd End-to-end Task Oriented Dialogue schema",
+            schema="seacrowd_tod",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "indosmd_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "index": datasets.Value("string"),
+                    "dialogue": [
+                        {
+                            "turn": datasets.Value("string"),
+                            "data": {
+                                "end_dialogue": datasets.Value("string"),
+                                "utterance": datasets.Value("string"),
+                                "delex_utterance": datasets.Value("string"),
+                                "requested": {
+                                    "distance": datasets.Value("string"),
+                                    "traffic_info": datasets.Value("string"),
+                                    "poi_type": datasets.Value("string"),
+                                    "address": datasets.Value("string"),
+                                    "poi": datasets.Value("string"),
+                                    "room": datasets.Value("string"),
+                                    "agenda": datasets.Value("string"),
+                                    "time": datasets.Value("string"),
+                                    "date": datasets.Value("string"),
+                                    "party": datasets.Value("string"),
+                                    "event": datasets.Value("string"),
+                                    "weather_attribute": datasets.Value("string"),
+                                    "location": datasets.Value("string"),
+                                },
+                                "slots": {
+                                    "distance": datasets.Value("string"),
+                                    "traffic_info": datasets.Value("string"),
+                                    "poi_type": datasets.Value("string"),
+                                    "address": datasets.Value("string"),
+                                    "poi": datasets.Value("string"),
+                                    "room": datasets.Value("string"),
+                                    "agenda": datasets.Value("string"),
+                                    "time": datasets.Value("string"),
+                                    "date": datasets.Value("string"),
+                                    "party": datasets.Value("string"),
+                                    "event": datasets.Value("string"),
+                                    "weather_attribute": datasets.Value("string"),
+                                    "location": datasets.Value("string"),
+                                },
+                            },
+                        }
+                    ],
+                    "scenario": {
+                        "kb": {
+                            "items": [
+                                {
+                                    "distance": datasets.Value("string"),
+                                    "traffic_info": datasets.Value("string"),
+                                    "poi_type": datasets.Value("string"),
+                                    "address": datasets.Value("string"),
+                                    "poi": datasets.Value("string"),
+                                    "room": datasets.Value("string"),
+                                    "agenda": datasets.Value("string"),
+                                    "time": datasets.Value("string"),
+                                    "date": datasets.Value("string"),
+                                    "party": datasets.Value("string"),
+                                    "event": datasets.Value("string"),
+                                    "monday": datasets.Value("string"),
+                                    "tuesday": datasets.Value("string"),
+                                    "wednesday": datasets.Value("string"),
+                                    "thursday": datasets.Value("string"),
+                                    "friday": datasets.Value("string"),
+                                    "saturday": datasets.Value("string"),
+                                    "sunday": datasets.Value("string"),
+                                    "today": datasets.Value("string"),
+                                    "location": datasets.Value("string"),
+                                }
+                            ],
+                            "column_names": [datasets.Value("string")],
+                            "kb_title": datasets.Value("string"),
+                        },
+                        "task": {"intent": datasets.Value("string")},
+                        "uuid": datasets.Value("string"),
+                    },
+                }
+            )
+        elif self.config.schema == "seacrowd_tod":
+            features = schemas.tod_features
+        else:
+            raise NotImplementedError(f"Schema {self.config.schema} has not been implemented")
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        key_slot_constant = ["distance", "traffic_info", "poi_type", "address", "poi", "room", "agenda", "time", "date", "party", "event", "weather_attribute", "location"]
+        key_kb_constant = ["distance", "traffic_info", "poi_type", "address", "poi", "room", "agenda", "time", "date", "party", "event", "monday", "tuesday", "wednesday", "thursday", "friday", "saturday", "sunday", "today", "location"]
+
+        with open(filepath, "r+") as fw:
+            data = json.loads(fw.read())
+
+        if self.config.schema == "source":
+            for idx, example in enumerate(data):
+                example["index"] = str(idx)
+                for i in range(len(example["dialogue"])):
+                    if "requested" not in example["dialogue"][i]["data"]:  # the difference between user and system utterance (user and system utterance is divided into each dict in the origin dataset)
+                        example["dialogue"][i]["data"]["requested"] = {}
+                        example["dialogue"][i]["data"]["slots"] = {}
+                        for key in key_slot_constant:
+                            example["dialogue"][i]["data"]["requested"][key] = ""
+                            example["dialogue"][i]["data"]["slots"][key] = ""
+                    else:
+                        for key in key_slot_constant:
+                            if key not in example["dialogue"][i]["data"]["requested"]:
+                                example["dialogue"][i]["data"]["requested"][key] = ""
+                            if key not in example["dialogue"][i]["data"]["slots"]:
+                                example["dialogue"][i]["data"]["slots"][key] = ""
+
+                if not example["scenario"]["kb"].get("items"):
+                    example["scenario"]["kb"]["items"] = []
+
+                for i in range(len(example["scenario"]["kb"]["items"])):
+                    for key in key_kb_constant:
+                        if key not in example["scenario"]["kb"]["items"][i]:
+                            example["scenario"]["kb"]["items"][i][key] = ""
+
+                yield str(idx), example
+
+        elif self.config.schema == "seacrowd_tod":
+            for idx, tod_dialogue in enumerate(data):
+                example = {}
+                example["dialogue_idx"] = idx
+
+                dialogue = []
+                # NOTE: the dialogue always started with `driver` as first utterance
+                for turn, i in enumerate(range(0, len(tod_dialogue["dialogue"]) + 2, 2)):
+                    dial = {}
+                    dial["turn_idx"] = turn
+
+                    # system_utterance properties
+                    dial["system_utterance"] = ""
+                    dial["system_acts"] = []
+                    if turn != 0:
+                        dial["system_utterance"] = tod_dialogue["dialogue"][i - 1]["data"]["utterance"]
+                    if i < len(tod_dialogue["dialogue"]):
+                        # NOTE: system_acts will be filled with every slot that has 'True' value on the origin dataset (on the requested field)
+                        for act in tod_dialogue["dialogue"][i + 1]["data"]["requested"]:
+                            if tod_dialogue["dialogue"][i + 1]["data"]["requested"][act]:
+                                dial["system_acts"].append([act])
+
+                    # user_utterance properties
+                    dial["turn_label"] = []
+                    dial["belief_state"] = []
+                    if i == len(tod_dialogue["dialogue"]):
+                        # case if turn_idx > len(dialogue) --> add dummy user_utterance
+                        dial["user_utterance"] = ""
+                    else:
+                        dial["user_utterance"] = tod_dialogue["dialogue"][i]["data"]["utterance"]
+                        # NOTE: belief_state will be filled with request act from `requested` field & inform act from `slots` field in the origin dataset
+                        for act in tod_dialogue["dialogue"][i + 1]["data"]["requested"]:
+                            if tod_dialogue["dialogue"][i + 1]["data"]["requested"][act]:
+                                dial["belief_state"].append({"slots": [["slot", act]], "act": "request"})
+                        for slot, slot_value in tod_dialogue["dialogue"][i + 1]["data"]["slots"].items():
+                            dial["belief_state"].append({"slots": [[slot, slot_value]], "act": "inform"})
+
+                    # append to dialogue
+                    dialogue.append(dial)
+                example["dialogue"] = dialogue
+                yield str(idx), example