| import json | |
| from pathlib import Path | |
| from typing import Dict, List, Tuple | |
| import datasets | |
| from seacrowd.utils.configs import SEACrowdConfig | |
| from seacrowd.utils.constants import Tasks | |
| _CITATION = """\ | |
| @article{majewska2022cross, | |
| title={Cross-lingual dialogue dataset creation via outline-based generation}, | |
| author={Majewska, Olga and Razumovskaia, Evgeniia and Ponti, Edoardo Maria and Vuli{\'c}, Ivan and Korhonen, Anna}, | |
| journal={arXiv preprint arXiv:2201.13405}, | |
| year={2022} | |
| } | |
| """ | |
| _LANGUAGES = ["ind"] | |
| _LOCAL = False | |
| _DATASETNAME = "cod" | |
| _DESCRIPTION = """\ | |
| Cross-lingual Outline-based Dialogue (COD) is a dataset comprised of manually generated, localized, and cross-lingually aligned Task-Oriented-Dialogue (TOD) data that served as the source of dialogue prompts. | |
| COD enables natural language understanding, dialogue state tracking, and end-to-end dialogue modeling and evaluation. | |
| Majewska et al. (2022) create COD using a novel outline-based annotation pipeline for multilingual TOD by Majewska et al. (2022). | |
| English Schema-Guided Dialogue (SGD; Shah et al., 2018; Rastogi et al., 2020) dataset is automatically sampled and mapped into outlines. The outlines are then paraphrased and adapted to the local target domain by human subjects. | |
| """ | |
| _HOMEPAGE = "https://github.com/cambridgeltl/COD" | |
| _LICENSE = "Unknown" | |
| _URLS = { | |
| _DATASETNAME: { | |
| "validation": "https://raw.githubusercontent.com/cambridgeltl/COD/main/id_dev.json", | |
| "test": "https://raw.githubusercontent.com/cambridgeltl/COD/main/id_test.json", | |
| }, | |
| } | |
| _SUPPORTED_TASKS = [Tasks.DIALOGUE_SYSTEM] | |
| _SOURCE_VERSION = "1.0.0" | |
| _SEACROWD_VERSION = "2024.06.20" | |
| class NewDataset(datasets.GeneratorBasedBuilder): | |
| """Cross-lingual Outline-based Dialogue (COD) is a dataset comprises manually generated, localised, and cross-lingually aligned Task-Oriented-Dialogue (TOD) data which served as the source of dialogue prompts.""" | |
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | |
| SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | |
| BUILDER_CONFIGS = [ | |
| SEACrowdConfig( | |
| name="cod_source", | |
| version=SOURCE_VERSION, | |
| description="Cross-lingual Outline-based Dialogue (COD) source schema", | |
| schema="source", | |
| subset_id="cod", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "cod_source" | |
| def _info(self) -> datasets.DatasetInfo: | |
| if self.config.schema == "source": | |
| features = datasets.Features( | |
| { | |
| "index": datasets.Value("string"), | |
| "dialogue_id": datasets.Value("string"), | |
| "services": [datasets.Value("string")], | |
| "turns": [ | |
| { | |
| "speaker": datasets.Value("string"), | |
| "utterance": datasets.Value("string"), | |
| "frames": [ | |
| { | |
| "actions": [ | |
| { | |
| "act": datasets.Value("string"), | |
| "slot": datasets.Value("string"), | |
| "values": [datasets.Value("string")], | |
| } | |
| ], | |
| "service": datasets.Value("string"), | |
| "slots": [ | |
| { | |
| "exclusive_end": datasets.Value("int32"), | |
| "slot": datasets.Value("string"), | |
| "start": datasets.Value("int32"), | |
| } | |
| ], | |
| "state": { | |
| "active_intent": datasets.Value("string"), | |
| "requested_slots": [datasets.Value("string")], | |
| "slot_values": [ | |
| {"slot": datasets.Value("string"), "values": [datasets.Value("string")]}, | |
| ], | |
| }, | |
| } | |
| ], | |
| } | |
| ], | |
| } | |
| ) | |
| else: | |
| raise NotImplementedError() | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | |
| urls = _URLS[_DATASETNAME] | |
| data_dir = dl_manager.download_and_extract(urls) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": data_dir["test"], | |
| "split": "test", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": data_dir["validation"], | |
| "split": "dev", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: | |
| 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 turn in example["turns"]: | |
| for frame in turn["frames"]: | |
| if "state" not in frame: | |
| continue | |
| ls_slot_values = [] | |
| for slot in frame["state"]["slot_values"]: | |
| ls_slot_values.append({"slot": slot, "values": frame["state"]["slot_values"][slot]}) | |
| frame["state"]["slot_values"] = ls_slot_values | |
| yield str(idx), example | |