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* refactor by pre-commit * reformatted by pre-commit * refactor code for globalwoz
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import os | ||
import json | ||
from pathlib import Path | ||
from typing import Dict, List, Tuple | ||
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import datasets | ||
import itertools | ||
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from seacrowd.utils import schemas | ||
from seacrowd.utils.configs import SEACrowdConfig | ||
from seacrowd.utils.constants import Tasks, Licenses | ||
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_CITATION = """\ | ||
@inproceedings{ding-etal-2022-globalwoz, | ||
title = "{G}lobal{W}o{Z}: Globalizing {M}ulti{W}o{Z} to Develop Multilingual Task-Oriented Dialogue Systems", | ||
author = "Ding, Bosheng and | ||
Hu, Junjie and | ||
Bing, Lidong and | ||
Aljunied, Mahani and | ||
Joty, Shafiq and | ||
Si, Luo and | ||
Miao, Chunyan", | ||
editor = "Muresan, Smaranda and | ||
Nakov, Preslav and | ||
Villavicencio, Aline", | ||
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", | ||
month = may, | ||
year = "2022", | ||
} | ||
""" | ||
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_DATASETNAME = "globalwoz" | ||
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_DESCRIPTION = """\ | ||
This is the data of the paper “GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems” accepted by ACL 2022. The dataset contains several sub-datasets in 20 languages and 3 schemes (F&E, E&F, F&F), including Indonesian (id), Thai (th), and Vietnamese (vi) language. The method is based on translating dialogue templates and filling them with local entities in the target language countries. | ||
""" | ||
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_HOMEPAGE = "https://github.com/bosheng2020/globalwoz" | ||
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_LANGUAGES = ["ind", "tha", "vie"] | ||
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_LICENSE = Licenses.UNKNOWN.value | ||
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_LOCAL = True | ||
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_URLS = {} | ||
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_SUPPORTED_TASKS = [Tasks.E2E_TASK_ORIENTED_DIALOGUE] | ||
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_SOURCE_VERSION = "2.0.0" | ||
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_SEACROWD_VERSION = "1.0.0" | ||
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def seacrowd_config_constructor(dial_type, lang, schema, version): | ||
if dial_type not in ["EandF", "FandE", "FandF"]: | ||
raise ValueError(f"Invalid dialogue type {dial_type}") | ||
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if lang == "": | ||
raise ValueError(f"Invalid lang {lang}") | ||
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if schema not in ["source", "seacrowd_tod"]: | ||
raise ValueError(f"Invalid schema: {schema}") | ||
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return SEACrowdConfig( | ||
name="globalwoz_{dial_type}_{lang}_{schema}".format(dial_type=dial_type, lang=lang, schema=schema), | ||
version=datasets.Version(version), | ||
description="GlobalWoZ schema for {schema}: {dial_type}_{lang}".format(schema=schema, dial_type=dial_type, lang=lang), | ||
schema=schema, | ||
subset_id="globalwoz_{dial_type}_{lang}".format(dial_type=dial_type, lang=lang), | ||
) | ||
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class GlobalWoZ(datasets.GeneratorBasedBuilder): | ||
"""This is the data of the paper “GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems” accepted by ACL 2022. | ||
The dataset contains several sub-datasets in 20 languages and 3 schemes (F&E, E&F, F&F), including Indonesian (id), Thai (th), | ||
and Vietnamese (vi) language. The method is based on translating dialogue templates and filling them with local entities in the target language countries. | ||
""" | ||
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) | ||
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) | ||
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BUILDER_CONFIGS = [ | ||
seacrowd_config_constructor(tod_format, lang, schema, _SOURCE_VERSION if schema == "source" else _SEACROWD_VERSION) for tod_format, lang, schema in itertools.product(("EandF", "FandE", "FandF"), ("id", "th", "vi"), ("source", "seacrowd_tod")) | ||
] | ||
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def _info(self) -> datasets.DatasetInfo: | ||
if self.config.schema == "source": | ||
features = datasets.Features( | ||
{ | ||
"id": datasets.Value("string"), | ||
"goal": { | ||
"attraction": datasets.Value("string"), | ||
"hospital": datasets.Value("string"), | ||
"hotel": datasets.Value("string"), | ||
"police": datasets.Value("string"), | ||
"restaurant": datasets.Value("string"), | ||
"taxi": datasets.Value("string"), | ||
"train": datasets.Value("string"), | ||
}, | ||
"log": [ | ||
{ | ||
"dialog_act": datasets.Value("string"), | ||
"metadata": datasets.Value("string"), | ||
"span_info": [[datasets.Value("string")]], | ||
"text": datasets.Value("string"), | ||
} | ||
], | ||
} | ||
) | ||
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elif self.config.schema == "seacrowd_tod": | ||
features = schemas.tod_features | ||
else: | ||
raise NotImplementedError() | ||
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return datasets.DatasetInfo( | ||
description=_DESCRIPTION, | ||
features=features, | ||
homepage=_HOMEPAGE, | ||
license=_LICENSE, | ||
citation=_CITATION, | ||
) | ||
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | ||
"""Returns SplitGenerators.""" | ||
_split_generators = [] | ||
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type_and_lang = {"dial_type": self.config.subset_id.split("_")[1].replace("and", "&"), "lang": self.config.subset_id.split("_")[2]} # globalwoz_{dial_type}_{lang} | ||
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if self.config.data_dir is None: | ||
raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.") | ||
else: | ||
data_dir = self.config.data_dir | ||
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if not os.path.exists(os.path.join(data_dir, f"{type_and_lang['dial_type']}_{type_and_lang['lang']}.json")): | ||
raise FileNotFoundError() | ||
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return [ | ||
datasets.SplitGenerator( | ||
name=datasets.Split.TRAIN, | ||
gen_kwargs={ | ||
# "filepath": data_dir + f"_{type_and_lang['dial_type']}_{type_and_lang['lang']}.json", | ||
"filepath": os.path.join(data_dir, f"{type_and_lang['dial_type']}_{type_and_lang['lang']}.json"), | ||
"split": "train", | ||
}, | ||
), | ||
] | ||
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: | ||
"""Yields examples as (key, example) tuples.""" | ||
# For local datasets you will have access to self.config.data_dir and self.config.data_files | ||
with open(filepath, "r+", encoding="utf8") as fw: | ||
data = json.load(fw) | ||
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if self.config.schema == "source": | ||
for idx, tod_dialogue in enumerate(data.values()): | ||
example = {} | ||
example["id"] = str(idx) | ||
example["goal"] = {} | ||
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for goal_key in ["attraction", "hospital", "hotel", "police", "restaurant", "taxi", "train"]: | ||
example["goal"][goal_key] = json.dumps(tod_dialogue["goal"][goal_key]) | ||
example["log"] = [] | ||
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for dial_log in tod_dialogue["log"]: | ||
dial = {} | ||
dial["dialog_act"] = json.dumps(dial_log["dialog_act"]) | ||
dial["metadata"] = json.dumps(dial_log["metadata"]) | ||
for i in range(len(dial_log["span_info"])): | ||
for j in range(len(dial_log["span_info"][i])): | ||
dial_log["span_info"][i][j] = str(dial_log["span_info"][i][j]) # casting to str | ||
dial["span_info"] = [[str(span)] if isinstance(span, str) else span for span in dial_log["span_info"]] | ||
dial["text"] = dial_log["text"] | ||
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example["log"].append(dial) | ||
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yield example["id"], example | ||
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elif self.config.schema == "seacrowd_tod": | ||
for idx, tod_dialogue in enumerate(data.values()): | ||
example = {} | ||
example["dialogue_idx"] = idx | ||
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dialogue = [] | ||
# NOTE: the dialogue always started with `user` as first utterance | ||
for turn, i in enumerate(range(0, len(tod_dialogue["log"]) + 2, 2)): | ||
dial = {} | ||
dial["turn_idx"] = turn | ||
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# system_utterance properties | ||
dial["system_utterance"] = "" | ||
dial["system_acts"] = [] | ||
if turn != 0: | ||
dial["system_utterance"] = tod_dialogue["log"][i - 1]["text"] | ||
if i < len(tod_dialogue["log"]): | ||
# NOTE: "system_acts will be populated with the `dialog_act` from the user utterance in the original dataset, as our schema dictates | ||
# that `system_acts` should represent the system's intended actions based on the user's utterance." | ||
for acts in tod_dialogue["log"][i]["dialog_act"].values(): | ||
for act in acts: | ||
dial["system_acts"].append([act[0]]) | ||
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# user_utterance properties | ||
dial["turn_label"] = [] # left as an empty array | ||
dial["belief_state"] = [] | ||
if i == len(tod_dialogue["log"]): | ||
# case if turn_idx > len(dialogue) --> add dummy user_utterance | ||
dial["user_utterance"] = "" | ||
else: | ||
dial["user_utterance"] = tod_dialogue["log"][i]["text"] | ||
# NOTE: "the belief_state will be populated with the `span_info` from the user utterance in the original dataset, as our schema dictates | ||
# that `belief_state` should represent the system's belief state based on the user's utterance." | ||
for span in tod_dialogue["log"][i]["span_info"]: | ||
if span[0].split("-")[1] == "request": # Request action | ||
dial["belief_state"].append({"slots": [["slot", span[1]]], "act": "request"}) | ||
else: | ||
dial["belief_state"].append({"slots": [[span[1], span[2]]], "act": span[0].split("-")[1]}) | ||
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# append to dialogue | ||
dialogue.append(dial) | ||
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example["dialogue"] = dialogue | ||
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yield example["dialogue_idx"], example |